A graphic means for assessing the ability of a screening test to discriminate between healthy and diseased persons; may also be used in other studies, e.g., distinguishing stimuli responses as to a faint stimuli or nonstimuli.
A statistical means of summarizing information from a series of measurements on one individual. It is frequently used in clinical pharmacology where the AUC from serum levels can be interpreted as the total uptake of whatever has been administered. As a plot of the concentration of a drug against time, after a single dose of medicine, producing a standard shape curve, it is a means of comparing the bioavailability of the same drug made by different companies. (From Winslade, Dictionary of Clinical Research, 1992)
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
Measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, environmental exposure and its effects, disease diagnosis, metabolic processes, substance abuse, pregnancy, cell line development, epidemiologic studies, etc.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.
The failure by the observer to measure or identify a phenomenon accurately, which results in an error. Sources for this may be due to the observer's missing an abnormality, or to faulty technique resulting in incorrect test measurement, or to misinterpretation of the data. Two varieties are inter-observer variation (the amount observers vary from one another when reporting on the same material) and intra-observer variation (the amount one observer varies between observations when reporting more than once on the same material).
Application of computer programs designed to assist the physician in solving a diagnostic problem.
Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.
A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.
Positive test results in subjects who do not possess the attribute for which the test is conducted. The labeling of healthy persons as diseased when screening in the detection of disease. (Last, A Dictionary of Epidemiology, 2d ed)
Examination of any part of the body for diagnostic purposes by means of X-RAYS or GAMMA RAYS, recording the image on a sensitized surface (such as photographic film).
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.
The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988)
The use of statistical and mathematical methods to analyze biological observations and phenomena.
Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.
Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.
Methods and procedures for the diagnosis of diseases or dysfunction of the endocrine glands or demonstration of their physiological processes.
Methods, procedures, and tests performed to diagnose disease, disordered function, or disability.
Research techniques that focus on study designs and data gathering methods in human and animal populations.
Diagnostic procedures, such as laboratory tests and x-rays, routinely performed on all individuals or specified categories of individuals in a specified situation, e.g., patients being admitted to the hospital. These include routine tests administered to neonates.
Molecular products metabolized and secreted by neoplastic tissue and characterized biochemically in cells or body fluids. They are indicators of tumor stage and grade as well as useful for monitoring responses to treatment and predicting recurrence. Many chemical groups are represented including hormones, antigens, amino and nucleic acids, enzymes, polyamines, and specific cell membrane proteins and lipids.
Improvement in the quality of an x-ray image by use of an intensifying screen, tube, or filter and by optimum exposure techniques. Digital processing methods are often employed.
An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.
Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.
A statistical analytic technique used with discrete dependent variables, concerned with separating sets of observed values and allocating new values. It is sometimes used instead of regression analysis.
Computer-based representation of physical systems and phenomena such as chemical processes.
Elements of limited time intervals, contributing to particular results or situations.
Methods and procedures for the diagnosis of diseases of the eye or of vision disorders.
Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.
The beginning of true OBSTETRIC LABOR which is characterized by the cyclic uterine contractions of increasing frequency, duration, and strength causing CERVICAL DILATATION to begin (LABOR STAGE, FIRST ).
The range or frequency distribution of a measurement in a population (of organisms, organs or things) that has not been selected for the presence of disease or abnormality.
Liver disease in which the normal microcirculation, the gross vascular anatomy, and the hepatic architecture have been variably destroyed and altered with fibrous septa surrounding regenerated or regenerating parenchymal nodules.
Mathematical or statistical procedures used as aids in making a decision. They are frequently used in medical decision-making.
A single lung lesion that is characterized by a small round mass of tissue, usually less than 1 cm in diameter, and can be detected by chest radiography. A solitary pulmonary nodule can be associated with neoplasm, tuberculosis, cyst, or other anomalies in the lung, the CHEST WALL, or the PLEURA.
Computer systems or networks designed to provide radiographic interpretive information.
A technique using antibodies for identifying or quantifying a substance. Usually the substance being studied serves as antigen both in antibody production and in measurement of antibody by the test substance.
Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.
Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.
Improvement of the quality of a picture by various techniques, including computer processing, digital filtering, echocardiographic techniques, light and ultrastructural MICROSCOPY, fluorescence spectrometry and microscopy, scintigraphy, and in vitro image processing at the molecular level.
A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.
Evaluation undertaken to assess the results or consequences of management and procedures used in combating disease in order to determine the efficacy, effectiveness, safety, and practicability of these interventions in individual cases or series.
An immunoassay utilizing an antibody labeled with an enzyme marker such as horseradish peroxidase. While either the enzyme or the antibody is bound to an immunosorbent substrate, they both retain their biologic activity; the change in enzyme activity as a result of the enzyme-antibody-antigen reaction is proportional to the concentration of the antigen and can be measured spectrophotometrically or with the naked eye. Many variations of the method have been developed.
Determination, by measurement or comparison with a standard, of the correct value of each scale reading on a meter or other measuring instrument; or determination of the settings of a control device that correspond to particular values of voltage, current, frequency or other output.
Conditions which produce injury or dysfunction of the second cranial or optic nerve, which is generally considered a component of the central nervous system. Damage to optic nerve fibers may occur at or near their origin in the retina, at the optic disk, or in the nerve, optic chiasm, optic tract, or lateral geniculate nuclei. Clinical manifestations may include decreased visual acuity and contrast sensitivity, impaired color vision, and an afferent pupillary defect.
Tomography using x-ray transmission and a computer algorithm to reconstruct the image.
Chemical analysis based on the phenomenon whereby light, passing through a medium with dispersed particles of a different refractive index from that of the medium, is attenuated in intensity by scattering. In turbidimetry, the intensity of light transmitted through the medium, the unscattered light, is measured. In nephelometry, the intensity of the scattered light is measured, usually, but not necessarily, at right angles to the incident light beam.
A basis of value established for the measure of quantity, weight, extent or quality, e.g. weight standards, standard solutions, methods, techniques, and procedures used in diagnosis and therapy.
Learning algorithms which are a set of related supervised computer learning methods that analyze data and recognize patterns, and used for classification and regression analysis.
A PEPTIDE that is secreted by the BRAIN and the HEART ATRIA, stored mainly in cardiac ventricular MYOCARDIUM. It can cause NATRIURESIS; DIURESIS; VASODILATION; and inhibits secretion of RENIN and ALDOSTERONE. It improves heart function. It contains 32 AMINO ACIDS.
A change in the CERVIX UTERI with respect to its readiness to relax. The cervix normally becomes softer, more flexible, more distensible, and shorter in the final weeks of PREGNANCY. These cervical changes can also be chemically induced (LABOR, INDUCED).
The study of chance processes or the relative frequency characterizing a chance process.
Organized periodic procedures performed on large groups of people for the purpose of detecting disease.
Methods to determine in patients the nature of a disease or disorder at its early stage of progression. Generally, early diagnosis improves PROGNOSIS and TREATMENT OUTCOME.

Validation of the Rockall risk scoring system in upper gastrointestinal bleeding. (1/7831)

BACKGROUND: Several scoring systems have been developed to predict the risk of rebleeding or death in patients with upper gastrointestinal bleeding (UGIB). These risk scoring systems have not been validated in a new patient population outside the clinical context of the original study. AIMS: To assess internal and external validity of a simple risk scoring system recently developed by Rockall and coworkers. METHODS: Calibration and discrimination were assessed as measures of validity of the scoring system. Internal validity was assessed using an independent, but similar patient sample studied by Rockall and coworkers, after developing the scoring system (Rockall's validation sample). External validity was assessed using patients admitted to several hospitals in Amsterdam (Vreeburg's validation sample). Calibration was evaluated by a chi2 goodness of fit test, and discrimination was evaluated by calculating the area under the receiver operating characteristic (ROC) curve. RESULTS: Calibration indicated a poor fit in both validation samples for the prediction of rebleeding (p<0.0001, Vreeburg; p=0.007, Rockall), but a better fit for the prediction of mortality in both validation samples (p=0.2, Vreeburg; p=0.3, Rockall). The areas under the ROC curves were rather low in both validation samples for the prediction of rebleeding (0.61, Vreeburg; 0.70, Rockall), but higher for the prediction of mortality (0.73, Vreeburg; 0.81, Rockall). CONCLUSIONS: The risk scoring system developed by Rockall and coworkers is a clinically useful scoring system for stratifying patients with acute UGIB into high and low risk categories for mortality. For the prediction of rebleeding, however, the performance of this scoring system was unsatisfactory.  (+info)

Computed radiography dual energy subtraction: performance evaluation when detecting low-contrast lung nodules in an anthropomorphic phantom. (2/7831)

A dedicated chest computed radiography (CR) system has an option of energy subtraction (ES) acquisition. Two imaging plates, rather than one, are separated by a copper filter to give a high-energy and low-energy image. This study compares the diagnostic accuracy of conventional computed radiography to that of ES obtained with two radiographic techniques. One soft tissue only image was obtained at the conventional CR technique (s = 254) and the second was obtained at twice the radiation exposure (s = 131) to reduce noise. An anthropomorphic phantom with superimposed low-contrast lung nodules was imaged 53 times for each radiographic technique. Fifteen images had no nodules; 38 images had a total of 90 nodules placed on the phantom. Three chest radiologists read the three sets of images in a receiver operating characteristic (ROC) study. Significant differences in Az were only found between (1) the higher exposure energy subtracted images and the conventional dose energy subtracted images (P = .095, 90% confidence), and (2) the conventional CR and the energy subtracted image obtained at the same technique (P = .024, 98% confidence). As a result of this study, energy subtracted images cannot be substituted for conventional CR images when detecting low-contrast nodules, even when twice the exposure is used to obtain them.  (+info)

Computerized analysis of abnormal asymmetry in digital chest radiographs: evaluation of potential utility. (3/7831)

The purpose of this study was to develop and test a computerized method for the fully automated analysis of abnormal asymmetry in digital posteroanterior (PA) chest radiographs. An automated lung segmentation method was used to identify the aerated lung regions in 600 chest radiographs. Minimal a priori lung morphology information was required for this gray-level thresholding-based segmentation. Consequently, segmentation was applicable to grossly abnormal cases. The relative areas of segmented right and left lung regions in each image were compared with the corresponding area distributions of normal images to determine the presence of abnormal asymmetry. Computerized diagnoses were compared with image ratings assigned by a radiologist. The ability of the automated method to distinguish normal from asymmetrically abnormal cases was evaluated by using receiver operating characteristic (ROC) analysis, which yielded an area under the ROC curve of 0.84. This automated method demonstrated promising performance in its ability to detect abnormal asymmetry in PA chest images. We believe this method could play a role in a picture archiving and communications (PACS) environment to immediately identify abnormal cases and to function as one component of a multifaceted computer-aided diagnostic scheme.  (+info)

Dose-response slope of forced oscillation and forced expiratory parameters in bronchial challenge testing. (4/7831)

In population studies, the provocative dose (PD) of bronchoconstrictor causing a significant decrement in lung function cannot be calculated for most subjects. Dose-response curves for carbachol were examined to determine whether this relationship can be summarized by means of a continuous index likely to be calculable for all subjects, namely the two-point dose response slope (DRS) of mean resistance (Rm) and resistance at 10 Hz (R10) measured by the forced oscillation technique (FOT). Five doses of carbachol (320 microg each) were inhaled by 71 patients referred for investigation of asthma (n=16), chronic cough (n=15), nasal polyposis (n=8), chronic rhinitis (n=8), dyspnoea (n=8), urticaria (n=5), post-anaphylactic shock (n=4) and miscellaneous conditions (n=7). FOT resistance and forced expiratory volume in one second (FEV1) were measured in close succession. The PD of carbachol leading to a fall in FEV1 > or = 20% (PD20) or a rise in Rm or R10 > or = 47% (PD47,Rm and PD47,R10) were calculated by interpolation. DRS for FEV1 (DRSFEV1), Rm (DRSRm) and R10 (DRSR10) were obtained as the percentage change at last dose divided by the total dose of carbachol. The sensitivity (Se) and specificity (Sp) of DRSRm, DRS10 delta%Rm and delta%R10 in detecting spirometric bronchial hyperresponsiveness (BHR, fall in FEV1 > or = 20%) were assessed by receiver operating characteristic (ROC) curves. There were 23 (32%) "spirometric" reactors. PD20 correlated strongly with DRSFEV1 (r=-0.962; p=0.0001); PD47,Rm correlated significantly with DRSRm (r=-0.648; p=0.0001) and PD47,R10 with DRSR10 (r=-0.552; p=0.0001). DRSFEV1 correlated significantly with both DRSRm (r=0.700; p=0.0001) and DRSR10 (r=0.784; p=0.0001). The Se and Sp of the various FOT indices to correctly detect spirometric BHR were as follows: DRSRm: Se=91.3%, Sp=81.2%; DRSR10: Se=91.3%, Sp=95.8%; delta%Rm: Se=86.9%, Sp=52.1%; and delta%R10: Se=91.3%, Sp=58.3%. Dose-response slopes of indices of forced oscillation technique resistance, especially the dose-response slope of resistance at 10Hz are proposed as simple quantitative indices of bronchial responsiveness which can be calculated for all subjects and that may be useful in occupational epidemiology.  (+info)

Relationship of glucose and insulin levels to the risk of myocardial infarction: a case-control study. (5/7831)

OBJECTIVE: To assess the relationship between dysglycemia and myocardial infarction in nondiabetic individuals. BACKGROUND: Nondiabetic hyperglycemia may be an important cardiac risk factor. The relationship between myocardial infarction and glucose, insulin, abdominal obesity, lipids and hypertension was therefore studied in South Asians-a group at high risk for coronary heart disease and diabetes. METHODS: Demographics, waist/hip ratio, fasting blood glucose (FBG), insulin, lipids and glucose tolerance were measured in 300 consecutive patients with a first myocardial infarction and 300 matched controls. RESULTS: Cases were more likely to have diabetes (OR 5.49; 95% CI 3.34, 9.01), impaired glucose tolerance (OR 4.08; 95% CI 2.31, 7.20) or impaired fasting glucose (OR 3.22; 95% CI 1.51, 6.85) than controls. Cases were 3.4 (95% CI 1.9, 5.8) and 6.0 (95% CI 3.3, 10.9) times more likely to have an FBG in the third and fourth quartile (5.2-6.3 and >6.3 mmol/1); after removing subjects with diabetes, impaired glucose tolerance and impaired fasting glucose, cases were 2.7 times (95% CI 1.5-4.8) more likely to have an FBG >5.2 mmol/l. A fasting glucose of 4.9 mmol/l best distinguished cases from controls (OR 3.42; 95% CI 2.42, 4.83). Glucose, abdominal obesity, lipids, hypertension and smoking were independent multivariate risk factors for myocardial infarction. In subjects without glucose intolerance, a 1.2 mmol/l (21 mg/dl) increase in postprandial glucose was independently associated with an increase in the odds of a myocardial infarction of 1.58 (95% CI 1.18, 2.12). CONCLUSIONS: A moderately elevated glucose level is a continuous risk factor for MI in nondiabetic South Asians with either normal or impaired glucose tolerance.  (+info)

13N-ammonia myocardial blood flow and uptake: relation to functional outcome of asynergic regions after revascularization. (6/7831)

OBJECTIVES: In this study we determined whether 13N-ammonia uptake measured late after injection provides additional insight into myocardial viability beyond its value as a myocardial blood flow tracer. BACKGROUND: Myocardial accumulation of 13N-ammonia is dependent on both regional blood flow and metabolic trapping. METHODS: Twenty-six patients with chronic coronary artery disease and left ventricular dysfunction underwent prerevascularization 13N-ammonia and 18F-deoxyglucose (FDG) positron emission tomography, and thallium single-photon emission computed tomography. Pre- and postrevascularization wall-motion abnormalities were assessed using gated cardiac magnetic resonance imaging or gated radionuclide angiography. RESULTS: Wall motion improved in 61 of 107 (57%) initially asynergic regions and remained abnormal in 46 after revascularization. Mean absolute myocardial blood flow was significantly higher in regions that improved compared to regions that did not improve after revascularization (0.63+/-0.27 vs. 0.52+/-0.25 ml/min/g, p < 0.04). Similarly, the magnitude of late 13N-ammonia uptake and FDG uptake was significantly higher in regions that improved (90+/-20% and 94+/-25%, respectively) compared to regions that did not improve after revascularization (67+/-24% and 71+/-25%, p < 0.001 for both, respectively). However, late 13N-ammonia uptake was a significantly better predictor of functional improvement after revascularization (area under the receiver operating characteristic [ROC] curve = 0.79) when compared to absolute blood flow (area under the ROC curve = 0.63, p < 0.05). In addition, there was a linear relationship between late 13N-ammonia uptake and FDG uptake (r = 0.68, p < 0.001) as well as thallium uptake (r = 0.76, p < 0.001) in all asynergic regions. CONCLUSIONS: These data suggest that beyond its value as a perfusion tracer, late 13N-ammonia uptake provides useful information regarding functional recovery after revascularization. The parallel relationship among 13N-ammonia, FDG, and thallium uptake supports the concept that uptake of 13N-ammonia as measured from the late images may provide important insight regarding cell membrane integrity and myocardial viability.  (+info)

Functional status and quality of life in patients with heart failure undergoing coronary bypass surgery after assessment of myocardial viability. (7/7831)

OBJECTIVES: The aim of this study was to evaluate whether preoperative clinical and test data could be used to predict the effects of myocardial revascularization on functional status and quality of life in patients with heart failure and ischemic LV dysfunction. BACKGROUND: Revascularization of viable myocardial segments has been shown to improve regional and global LV function. The effects of revascularization on exercise capacity and quality of life (QOL) are not well defined. METHODS: Sixty three patients (51 men, age 66+/-9 years) with moderate or worse LV dysfunction (LVEF 0.28+/-0.07) and symptomatic heart failure were studied before and after coronary artery bypass surgery. All patients underwent preoperative positron emission tomography (PET) using FDG and Rb-82 before and after dipyridamole stress; the extent of viable myocardium by PET was defined by the number of segments with metabolism-perfusion mismatch or ischemia. Dobutamine echocardiography (DbE) was performed in 47 patients; viability was defined by augmentation at low dose or the development of new or worsening wall motion abnormalities. Functional class, exercise testing and a QOL score (Nottingham Health Profile) were obtained at baseline and follow-up. RESULTS: Patients had wall motion abnormalities in 83+/-18% of LV segments. A mismatch pattern was identified in 12+/-15% of LV segments, and PET evidence of viability was detected in 30+/-21% of the LV. Viability was reported in 43+/-18% of the LV by DbE. The difference between pre- and postoperative exercise capacity ranged from a reduction of 2.8 to an augmentation of 5.2 METS. The degree of improvement of exercise capacity correlated with the extent of viability by PET (r = 0.54, p = 0.0001) but not the extent of viable myocardium by DbE (r = 0.02, p = 0.92). The area under the ROC curve for PET (0.76) exceeded that for DbE (0.66). In a multiple linear regression, the extent of viability by PET and nitrate use were the only independent predictors of improvement of exercise capacity (model r = 0.63, p = 0.0001). Change in Functional Class correlated weakly with the change in exercise capacity (r = 0.25), extent of viable myocardium by PET (r = 0.23) and extent of viability by DbE (r = 0.31). Four components of the quality of life score (energy, pain, emotion and mobility status) significantly improved over follow-up, but no correlations could be identified between quality of life scores and the results of preoperative testing or changes in exercise capacity. CONCLUSIONS: In patients with LV dysfunction, improvement of exercise capacity correlates with the extent of viable myocardium. Quality of life improves in most patients undergoing revascularization. However, its measurement by this index does not correlate with changes in other parameters nor is it readily predictable.  (+info)

Cardiac metaiodobenzylguanidine uptake in patients with moderate chronic heart failure: relationship with peak oxygen uptake and prognosis. (8/7831)

OBJECTIVES: This prospective study was undertaken to correlate early and late metaiodobenzylguanidine (MIBG) cardiac uptake with cardiac hemodynamics and exercise capacity in patients with heart failure and to compare their prognostic values with that of peak oxygen uptake (VO2). BACKGROUND: The cardiac fixation of MIBG reflects presynaptic uptake and is reduced in heart failure. Whether it is related to exercise capacity and has better prognostic value than peak VO2 is unknown. METHODS: Ninety-three patients with heart failure (ejection fraction <45%) were studied with planar MIBG imaging, cardiopulmonary exercise tests and hemodynamics (n = 44). Early (20 min) and late (4 h) MIBG acquisition, as well as their ratio (washout, WO) were determined. Prognostic value was assessed by survival curves (Kaplan-Meier method) and uni- and multivariate Cox analyses. RESULTS: Late cardiac MIBG uptake was reduced (131+/-20%, normal values 192+/-42%) and correlated with ejection fraction (r = 0.49), cardiac index (r = 0.40) and pulmonary wedge pressure (r = -0.35). There was a significant correlation between peak VO2 and MIBG uptake (r = 0.41, p < 0.0001). With a mean follow-up of 10+/-8 months, both late MIBG uptake (p = 0.04) and peak VO2 (p < 0.0001) were predictive of death or heart transplantation, but only peak VO2 emerged by multivariate analysis. Neither early MIBG uptake nor WO yielded significant insights beyond those provided by late MIBG uptake. CONCLUSIONS: Metaiodobenzylguanidine uptake has prognostic value in patients with wide ranges of heart failure, but peak VO2 remains the most powerful prognostic index.  (+info)

A Receiver Operating Characteristic (ROC) curve is a graphical representation used in medical decision-making and statistical analysis to illustrate the performance of a binary classifier system, such as a diagnostic test or a machine learning algorithm. It's a plot that shows the tradeoff between the true positive rate (sensitivity) and the false positive rate (1 - specificity) for different threshold settings.

The x-axis of an ROC curve represents the false positive rate (the proportion of negative cases incorrectly classified as positive), while the y-axis represents the true positive rate (the proportion of positive cases correctly classified as positive). Each point on the curve corresponds to a specific decision threshold, with higher points indicating better performance.

The area under the ROC curve (AUC) is a commonly used summary measure that reflects the overall performance of the classifier. An AUC value of 1 indicates perfect discrimination between positive and negative cases, while an AUC value of 0.5 suggests that the classifier performs no better than chance.

ROC curves are widely used in healthcare to evaluate diagnostic tests, predictive models, and screening tools for various medical conditions, helping clinicians make informed decisions about patient care based on the balance between sensitivity and specificity.

The term "Area Under Curve" (AUC) is commonly used in the medical field, particularly in the analysis of diagnostic tests or pharmacokinetic studies. The AUC refers to the mathematical calculation of the area between a curve and the x-axis in a graph, typically representing a concentration-time profile.

In the context of diagnostic tests, the AUC is used to evaluate the performance of a test by measuring the entire two-dimensional area underneath the receiver operating characteristic (ROC) curve, which plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. The AUC ranges from 0 to 1, where a higher AUC indicates better test performance:

* An AUC of 0.5 suggests that the test is no better than chance.
* An AUC between 0.7 and 0.8 implies moderate accuracy.
* An AUC between 0.8 and 0.9 indicates high accuracy.
* An AUC greater than 0.9 signifies very high accuracy.

In pharmacokinetic studies, the AUC is used to assess drug exposure over time by calculating the area under a plasma concentration-time curve (AUC(0-t) or AUC(0-\∞)) following drug administration. This value can help determine dosing regimens and evaluate potential drug interactions:

* AUC(0-t): Represents the area under the plasma concentration-time curve from time zero to the last measurable concentration (t).
* AUC(0-\∞): Refers to the area under the plasma concentration-time curve from time zero to infinity, which estimates total drug exposure.

Sensitivity and specificity are statistical measures used to describe the performance of a diagnostic test or screening tool in identifying true positive and true negative results.

* Sensitivity refers to the proportion of people who have a particular condition (true positives) who are correctly identified by the test. It is also known as the "true positive rate" or "recall." A highly sensitive test will identify most or all of the people with the condition, but may also produce more false positives.
* Specificity refers to the proportion of people who do not have a particular condition (true negatives) who are correctly identified by the test. It is also known as the "true negative rate." A highly specific test will identify most or all of the people without the condition, but may also produce more false negatives.

In medical testing, both sensitivity and specificity are important considerations when evaluating a diagnostic test. High sensitivity is desirable for screening tests that aim to identify as many cases of a condition as possible, while high specificity is desirable for confirmatory tests that aim to rule out the condition in people who do not have it.

It's worth noting that sensitivity and specificity are often influenced by factors such as the prevalence of the condition in the population being tested, the threshold used to define a positive result, and the reliability and validity of the test itself. Therefore, it's important to consider these factors when interpreting the results of a diagnostic test.

The Predictive Value of Tests, specifically the Positive Predictive Value (PPV) and Negative Predictive Value (NPV), are measures used in diagnostic tests to determine the probability that a positive or negative test result is correct.

Positive Predictive Value (PPV) is the proportion of patients with a positive test result who actually have the disease. It is calculated as the number of true positives divided by the total number of positive results (true positives + false positives). A higher PPV indicates that a positive test result is more likely to be a true positive, and therefore the disease is more likely to be present.

Negative Predictive Value (NPV) is the proportion of patients with a negative test result who do not have the disease. It is calculated as the number of true negatives divided by the total number of negative results (true negatives + false negatives). A higher NPV indicates that a negative test result is more likely to be a true negative, and therefore the disease is less likely to be present.

The predictive value of tests depends on the prevalence of the disease in the population being tested, as well as the sensitivity and specificity of the test. A test with high sensitivity and specificity will generally have higher predictive values than a test with low sensitivity and specificity. However, even a highly sensitive and specific test can have low predictive values if the prevalence of the disease is low in the population being tested.

Reproducibility of results in a medical context refers to the ability to obtain consistent and comparable findings when a particular experiment or study is repeated, either by the same researcher or by different researchers, following the same experimental protocol. It is an essential principle in scientific research that helps to ensure the validity and reliability of research findings.

In medical research, reproducibility of results is crucial for establishing the effectiveness and safety of new treatments, interventions, or diagnostic tools. It involves conducting well-designed studies with adequate sample sizes, appropriate statistical analyses, and transparent reporting of methods and findings to allow other researchers to replicate the study and confirm or refute the results.

The lack of reproducibility in medical research has become a significant concern in recent years, as several high-profile studies have failed to produce consistent findings when replicated by other researchers. This has led to increased scrutiny of research practices and a call for greater transparency, rigor, and standardization in the conduct and reporting of medical research.

A biological marker, often referred to as a biomarker, is a measurable indicator that reflects the presence or severity of a disease state, or a response to a therapeutic intervention. Biomarkers can be found in various materials such as blood, tissues, or bodily fluids, and they can take many forms, including molecular, histologic, radiographic, or physiological measurements.

In the context of medical research and clinical practice, biomarkers are used for a variety of purposes, such as:

1. Diagnosis: Biomarkers can help diagnose a disease by indicating the presence or absence of a particular condition. For example, prostate-specific antigen (PSA) is a biomarker used to detect prostate cancer.
2. Monitoring: Biomarkers can be used to monitor the progression or regression of a disease over time. For instance, hemoglobin A1c (HbA1c) levels are monitored in diabetes patients to assess long-term blood glucose control.
3. Predicting: Biomarkers can help predict the likelihood of developing a particular disease or the risk of a negative outcome. For example, the presence of certain genetic mutations can indicate an increased risk for breast cancer.
4. Response to treatment: Biomarkers can be used to evaluate the effectiveness of a specific treatment by measuring changes in the biomarker levels before and after the intervention. This is particularly useful in personalized medicine, where treatments are tailored to individual patients based on their unique biomarker profiles.

It's important to note that for a biomarker to be considered clinically valid and useful, it must undergo rigorous validation through well-designed studies, including demonstrating sensitivity, specificity, reproducibility, and clinical relevance.

An algorithm is not a medical term, but rather a concept from computer science and mathematics. In the context of medicine, algorithms are often used to describe step-by-step procedures for diagnosing or managing medical conditions. These procedures typically involve a series of rules or decision points that help healthcare professionals make informed decisions about patient care.

For example, an algorithm for diagnosing a particular type of heart disease might involve taking a patient's medical history, performing a physical exam, ordering certain diagnostic tests, and interpreting the results in a specific way. By following this algorithm, healthcare professionals can ensure that they are using a consistent and evidence-based approach to making a diagnosis.

Algorithms can also be used to guide treatment decisions. For instance, an algorithm for managing diabetes might involve setting target blood sugar levels, recommending certain medications or lifestyle changes based on the patient's individual needs, and monitoring the patient's response to treatment over time.

Overall, algorithms are valuable tools in medicine because they help standardize clinical decision-making and ensure that patients receive high-quality care based on the latest scientific evidence.

Statistical models are mathematical representations that describe the relationship between variables in a given dataset. They are used to analyze and interpret data in order to make predictions or test hypotheses about a population. In the context of medicine, statistical models can be used for various purposes such as:

1. Disease risk prediction: By analyzing demographic, clinical, and genetic data using statistical models, researchers can identify factors that contribute to an individual's risk of developing certain diseases. This information can then be used to develop personalized prevention strategies or early detection methods.

2. Clinical trial design and analysis: Statistical models are essential tools for designing and analyzing clinical trials. They help determine sample size, allocate participants to treatment groups, and assess the effectiveness and safety of interventions.

3. Epidemiological studies: Researchers use statistical models to investigate the distribution and determinants of health-related events in populations. This includes studying patterns of disease transmission, evaluating public health interventions, and estimating the burden of diseases.

4. Health services research: Statistical models are employed to analyze healthcare utilization, costs, and outcomes. This helps inform decisions about resource allocation, policy development, and quality improvement initiatives.

5. Biostatistics and bioinformatics: In these fields, statistical models are used to analyze large-scale molecular data (e.g., genomics, proteomics) to understand biological processes and identify potential therapeutic targets.

In summary, statistical models in medicine provide a framework for understanding complex relationships between variables and making informed decisions based on data-driven insights.

Prospective studies, also known as longitudinal studies, are a type of cohort study in which data is collected forward in time, following a group of individuals who share a common characteristic or exposure over a period of time. The researchers clearly define the study population and exposure of interest at the beginning of the study and follow up with the participants to determine the outcomes that develop over time. This type of study design allows for the investigation of causal relationships between exposures and outcomes, as well as the identification of risk factors and the estimation of disease incidence rates. Prospective studies are particularly useful in epidemiology and medical research when studying diseases with long latency periods or rare outcomes.

Observer variation, also known as inter-observer variability or measurement agreement, refers to the difference in observations or measurements made by different observers or raters when evaluating the same subject or phenomenon. It is a common issue in various fields such as medicine, research, and quality control, where subjective assessments are involved.

In medical terms, observer variation can occur in various contexts, including:

1. Diagnostic tests: Different radiologists may interpret the same X-ray or MRI scan differently, leading to variations in diagnosis.
2. Clinical trials: Different researchers may have different interpretations of clinical outcomes or adverse events, affecting the consistency and reliability of trial results.
3. Medical records: Different healthcare providers may document medical histories, physical examinations, or treatment plans differently, leading to inconsistencies in patient care.
4. Pathology: Different pathologists may have varying interpretations of tissue samples or laboratory tests, affecting diagnostic accuracy.

Observer variation can be minimized through various methods, such as standardized assessment tools, training and calibration of observers, and statistical analysis of inter-rater reliability.

Computer-assisted diagnosis (CAD) is the use of computer systems to aid in the diagnostic process. It involves the use of advanced algorithms and data analysis techniques to analyze medical images, laboratory results, and other patient data to help healthcare professionals make more accurate and timely diagnoses. CAD systems can help identify patterns and anomalies that may be difficult for humans to detect, and they can provide second opinions and flag potential errors or uncertainties in the diagnostic process.

CAD systems are often used in conjunction with traditional diagnostic methods, such as physical examinations and patient interviews, to provide a more comprehensive assessment of a patient's health. They are commonly used in radiology, pathology, cardiology, and other medical specialties where imaging or laboratory tests play a key role in the diagnostic process.

While CAD systems can be very helpful in the diagnostic process, they are not infallible and should always be used as a tool to support, rather than replace, the expertise of trained healthcare professionals. It's important for medical professionals to use their clinical judgment and experience when interpreting CAD results and making final diagnoses.

Retrospective studies, also known as retrospective research or looking back studies, are a type of observational study that examines data from the past to draw conclusions about possible causal relationships between risk factors and outcomes. In these studies, researchers analyze existing records, medical charts, or previously collected data to test a hypothesis or answer a specific research question.

Retrospective studies can be useful for generating hypotheses and identifying trends, but they have limitations compared to prospective studies, which follow participants forward in time from exposure to outcome. Retrospective studies are subject to biases such as recall bias, selection bias, and information bias, which can affect the validity of the results. Therefore, retrospective studies should be interpreted with caution and used primarily to generate hypotheses for further testing in prospective studies.

Prognosis is a medical term that refers to the prediction of the likely outcome or course of a disease, including the chances of recovery or recurrence, based on the patient's symptoms, medical history, physical examination, and diagnostic tests. It is an important aspect of clinical decision-making and patient communication, as it helps doctors and patients make informed decisions about treatment options, set realistic expectations, and plan for future care.

Prognosis can be expressed in various ways, such as percentages, categories (e.g., good, fair, poor), or survival rates, depending on the nature of the disease and the available evidence. However, it is important to note that prognosis is not an exact science and may vary depending on individual factors, such as age, overall health status, and response to treatment. Therefore, it should be used as a guide rather than a definitive forecast.

Logistic models, specifically logistic regression models, are a type of statistical analysis used in medical and epidemiological research to identify the relationship between the risk of a certain health outcome or disease (dependent variable) and one or more independent variables, such as demographic factors, exposure variables, or other clinical measurements.

In contrast to linear regression models, logistic regression models are used when the dependent variable is binary or dichotomous in nature, meaning it can only take on two values, such as "disease present" or "disease absent." The model uses a logistic function to estimate the probability of the outcome based on the independent variables.

Logistic regression models are useful for identifying risk factors and estimating the strength of associations between exposures and health outcomes, adjusting for potential confounders, and predicting the probability of an outcome given certain values of the independent variables. They can also be used to develop clinical prediction rules or scores that can aid in decision-making and patient care.

A "false positive reaction" in medical testing refers to a situation where a diagnostic test incorrectly indicates the presence of a specific condition or disease in an individual who does not actually have it. This occurs when the test results give a positive outcome, while the true health status of the person is negative or free from the condition being tested for.

False positive reactions can be caused by various factors including:

1. Presence of unrelated substances that interfere with the test result (e.g., cross-reactivity between similar molecules).
2. Low specificity of the test, which means it may detect other conditions or irrelevant factors as positive.
3. Contamination during sample collection, storage, or analysis.
4. Human errors in performing or interpreting the test results.

False positive reactions can have significant consequences, such as unnecessary treatments, anxiety, and increased healthcare costs. Therefore, it is essential to confirm any positive test result with additional tests or clinical evaluations before making a definitive diagnosis.

Radiography is a diagnostic technique that uses X-rays, gamma rays, or similar types of radiation to produce images of the internal structures of the body. It is a non-invasive procedure that can help healthcare professionals diagnose and monitor a wide range of medical conditions, including bone fractures, tumors, infections, and foreign objects lodged in the body.

During a radiography exam, a patient is positioned between an X-ray machine and a special film or digital detector. The machine emits a beam of radiation that passes through the body and strikes the film or detector, creating a shadow image of the internal structures. Denser tissues, such as bones, block more of the radiation and appear white on the image, while less dense tissues, such as muscles and organs, allow more of the radiation to pass through and appear darker.

Radiography is a valuable tool in modern medicine, but it does involve exposure to ionizing radiation, which can carry some risks. Healthcare professionals take steps to minimize these risks by using the lowest possible dose of radiation necessary to produce a diagnostic image, and by shielding sensitive areas of the body with lead aprons or other protective devices.

Statistical data interpretation involves analyzing and interpreting numerical data in order to identify trends, patterns, and relationships. This process often involves the use of statistical methods and tools to organize, summarize, and draw conclusions from the data. The goal is to extract meaningful insights that can inform decision-making, hypothesis testing, or further research.

In medical contexts, statistical data interpretation is used to analyze and make sense of large sets of clinical data, such as patient outcomes, treatment effectiveness, or disease prevalence. This information can help healthcare professionals and researchers better understand the relationships between various factors that impact health outcomes, develop more effective treatments, and identify areas for further study.

Some common statistical methods used in data interpretation include descriptive statistics (e.g., mean, median, mode), inferential statistics (e.g., hypothesis testing, confidence intervals), and regression analysis (e.g., linear, logistic). These methods can help medical professionals identify patterns and trends in the data, assess the significance of their findings, and make evidence-based recommendations for patient care or public health policy.

A Severity of Illness Index is a measurement tool used in healthcare to assess the severity of a patient's condition and the risk of mortality or other adverse outcomes. These indices typically take into account various physiological and clinical variables, such as vital signs, laboratory values, and co-morbidities, to generate a score that reflects the patient's overall illness severity.

Examples of Severity of Illness Indices include the Acute Physiology and Chronic Health Evaluation (APACHE) system, the Simplified Acute Physiology Score (SAPS), and the Mortality Probability Model (MPM). These indices are often used in critical care settings to guide clinical decision-making, inform prognosis, and compare outcomes across different patient populations.

It is important to note that while these indices can provide valuable information about a patient's condition, they should not be used as the sole basis for clinical decision-making. Rather, they should be considered in conjunction with other factors, such as the patient's overall clinical presentation, treatment preferences, and goals of care.

Risk assessment in the medical context refers to the process of identifying, evaluating, and prioritizing risks to patients, healthcare workers, or the community related to healthcare delivery. It involves determining the likelihood and potential impact of adverse events or hazards, such as infectious diseases, medication errors, or medical devices failures, and implementing measures to mitigate or manage those risks. The goal of risk assessment is to promote safe and high-quality care by identifying areas for improvement and taking action to minimize harm.

Biometry, also known as biometrics, is the scientific study of measurements and statistical analysis of living organisms. In a medical context, biometry is often used to refer to the measurement and analysis of physical characteristics or features of the human body, such as height, weight, blood pressure, heart rate, and other physiological variables. These measurements can be used for a variety of purposes, including diagnosis, treatment planning, monitoring disease progression, and research.

In addition to physical measurements, biometry may also refer to the use of statistical methods to analyze biological data, such as genetic information or medical images. This type of analysis can help researchers and clinicians identify patterns and trends in large datasets, and make predictions about health outcomes or treatment responses.

Overall, biometry is an important tool in modern medicine, as it allows healthcare professionals to make more informed decisions based on data and evidence.

Computer-assisted image interpretation is the use of computer algorithms and software to assist healthcare professionals in analyzing and interpreting medical images. These systems use various techniques such as pattern recognition, machine learning, and artificial intelligence to help identify and highlight abnormalities or patterns within imaging data, such as X-rays, CT scans, MRI, and ultrasound images. The goal is to increase the accuracy, consistency, and efficiency of image interpretation, while also reducing the potential for human error. It's important to note that these systems are intended to assist healthcare professionals in their decision making process and not to replace them.

"Likelihood functions" is a statistical concept that is used in medical research and other fields to estimate the probability of obtaining a given set of data, given a set of assumptions or parameters. In other words, it is a function that describes how likely it is to observe a particular outcome or result, based on a set of model parameters.

More formally, if we have a statistical model that depends on a set of parameters θ, and we observe some data x, then the likelihood function is defined as:

L(θ | x) = P(x | θ)

This means that the likelihood function describes the probability of observing the data x, given a particular value of the parameter vector θ. By convention, the likelihood function is often expressed as a function of the parameters, rather than the data, so we might instead write:

L(θ) = P(x | θ)

The likelihood function can be used to estimate the values of the model parameters that are most consistent with the observed data. This is typically done by finding the value of θ that maximizes the likelihood function, which is known as the maximum likelihood estimator (MLE). The MLE has many desirable statistical properties, including consistency, efficiency, and asymptotic normality.

In medical research, likelihood functions are often used in the context of Bayesian analysis, where they are combined with prior distributions over the model parameters to obtain posterior distributions that reflect both the observed data and prior knowledge or assumptions about the parameter values. This approach is particularly useful when there is uncertainty or ambiguity about the true value of the parameters, as it allows researchers to incorporate this uncertainty into their analyses in a principled way.

Diagnostic techniques in endocrinology are methods used to identify and diagnose various endocrine disorders. These techniques include:

1. Hormone measurements: Measuring the levels of hormones in blood, urine, or saliva can help identify excess or deficiency of specific hormones. This is often done through immunoassays, which use antibodies to detect and quantify hormones.

2. Provocative and suppression tests: These tests involve administering a medication that stimulates or suppresses the release of a particular hormone. Blood samples are taken before and after the medication is given to assess changes in hormone levels. Examples include the glucose tolerance test for diabetes, the ACTH stimulation test for adrenal insufficiency, and the thyroid suppression test for hyperthyroidism.

3. Imaging studies: Various imaging techniques can be used to visualize endocrine glands and identify structural abnormalities such as tumors or nodules. These include X-rays, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine scans using radioactive tracers.

4. Genetic testing: Molecular genetic tests can be used to identify genetic mutations associated with certain endocrine disorders, such as multiple endocrine neoplasia type 1 or 2, or congenital adrenal hyperplasia.

5. Biopsy: In some cases, a small sample of tissue may be removed from an endocrine gland for microscopic examination (biopsy). This can help confirm the presence of cancer or other abnormalities.

6. Functional tests: These tests assess the ability of an endocrine gland to produce and secrete hormones in response to various stimuli. Examples include the glucagon stimulation test for gastrinoma and the calcium infusion test for hyperparathyroidism.

7. Wearable monitoring devices: Continuous glucose monitoring systems (CGMS) are wearable devices that measure interstitial glucose levels continuously over several days, providing valuable information about glycemic control in patients with diabetes.

Diagnostic techniques and procedures are methods used by medical professionals to identify the cause of symptoms, illnesses, or diseases. These can include physical examinations, patient interviews, review of medical history, and various diagnostic tests. Diagnostic tests may involve invasive procedures such as biopsies or surgical interventions, or non-invasive imaging techniques like X-rays, CT scans, MRI scans, or ultrasounds. Functional tests, such as stress testing or electroencephalogram (EEG), can also be used to evaluate the functioning of specific organs or systems in the body. Laboratory tests, including blood tests, urine tests, and genetic tests, are also common diagnostic procedures. The choice of diagnostic technique or procedure depends on the presenting symptoms, the patient's medical history, and the suspected underlying condition.

Epidemiologic methods are systematic approaches used to investigate and understand the distribution, determinants, and outcomes of health-related events or diseases in a population. These methods are applied to study the patterns of disease occurrence and transmission, identify risk factors and causes, and evaluate interventions for prevention and control. The core components of epidemiologic methods include:

1. Descriptive Epidemiology: This involves the systematic collection and analysis of data on the who, what, when, and where of health events to describe their distribution in a population. It includes measures such as incidence, prevalence, mortality, and morbidity rates, as well as geographic and temporal patterns.

2. Analytical Epidemiology: This involves the use of statistical methods to examine associations between potential risk factors and health outcomes. It includes observational studies (cohort, case-control, cross-sectional) and experimental studies (randomized controlled trials). The goal is to identify causal relationships and quantify the strength of associations.

3. Experimental Epidemiology: This involves the design and implementation of interventions or experiments to test hypotheses about disease prevention and control. It includes randomized controlled trials, community trials, and other experimental study designs.

4. Surveillance and Monitoring: This involves ongoing systematic collection, analysis, and interpretation of health-related data for early detection, tracking, and response to health events or diseases.

5. Ethical Considerations: Epidemiologic studies must adhere to ethical principles such as respect for autonomy, beneficence, non-maleficence, and justice. This includes obtaining informed consent, ensuring confidentiality, and minimizing harm to study participants.

Overall, epidemiologic methods provide a framework for investigating and understanding the complex interplay between host, agent, and environmental factors that contribute to the occurrence of health-related events or diseases in populations.

'Diagnostic tests, routine' is a medical term that refers to standard or commonly used tests that are performed to help diagnose, monitor, or manage a patient's health condition. These tests are typically simple, non-invasive, and safe, and they may be ordered as part of a regular check-up or when a patient presents with specific symptoms.

Routine diagnostic tests may include:

1. Complete Blood Count (CBC): A test that measures the number of red and white blood cells, platelets, and hemoglobin in the blood. It can help diagnose conditions such as anemia, infection, and inflammation.
2. Urinalysis: A test that examines a urine sample for signs of infection, kidney disease, or other medical conditions.
3. Blood Chemistry Tests: Also known as a chemistry panel or comprehensive metabolic panel, this test measures various chemicals in the blood such as glucose, electrolytes, and enzymes to evaluate organ function and overall health.
4. Electrocardiogram (ECG): A test that records the electrical activity of the heart, which can help diagnose heart conditions such as arrhythmias or heart attacks.
5. Chest X-ray: An imaging test that creates pictures of the structures inside the chest, including the heart, lungs, and bones, to help diagnose conditions such as pneumonia or lung cancer.
6. Fecal Occult Blood Test (FOBT): A test that checks for hidden blood in the stool, which can be a sign of colon cancer or other gastrointestinal conditions.
7. Pap Smear: A test that collects cells from the cervix to check for abnormalities that may indicate cervical cancer or other gynecological conditions.

These are just a few examples of routine diagnostic tests that healthcare providers may order. The specific tests ordered will depend on the patient's age, sex, medical history, and current symptoms.

Tumor markers are substances that can be found in the body and their presence can indicate the presence of certain types of cancer or other conditions. Biological tumor markers refer to those substances that are produced by cancer cells or by other cells in response to cancer or certain benign (non-cancerous) conditions. These markers can be found in various bodily fluids such as blood, urine, or tissue samples.

Examples of biological tumor markers include:

1. Proteins: Some tumor markers are proteins that are produced by cancer cells or by other cells in response to the presence of cancer. For example, prostate-specific antigen (PSA) is a protein produced by normal prostate cells and in higher amounts by prostate cancer cells.
2. Genetic material: Tumor markers can also include genetic material such as DNA, RNA, or microRNA that are shed by cancer cells into bodily fluids. For example, circulating tumor DNA (ctDNA) is genetic material from cancer cells that can be found in the bloodstream.
3. Metabolites: Tumor markers can also include metabolic products produced by cancer cells or by other cells in response to cancer. For example, lactate dehydrogenase (LDH) is an enzyme that is released into the bloodstream when cancer cells break down glucose for energy.

It's important to note that tumor markers are not specific to cancer and can be elevated in non-cancerous conditions as well. Therefore, they should not be used alone to diagnose cancer but rather as a tool in conjunction with other diagnostic tests and clinical evaluations.

Radiographic image enhancement refers to the process of improving the quality and clarity of radiographic images, such as X-rays, CT scans, or MRI images, through various digital techniques. These techniques may include adjusting contrast, brightness, and sharpness, as well as removing noise and artifacts that can interfere with image interpretation.

The goal of radiographic image enhancement is to provide medical professionals with clearer and more detailed images, which can help in the diagnosis and treatment of medical conditions. This process may be performed using specialized software or hardware tools, and it requires a strong understanding of imaging techniques and the specific needs of medical professionals.

Medical Definition:

"Risk factors" are any attribute, characteristic or exposure of an individual that increases the likelihood of developing a disease or injury. They can be divided into modifiable and non-modifiable risk factors. Modifiable risk factors are those that can be changed through lifestyle choices or medical treatment, while non-modifiable risk factors are inherent traits such as age, gender, or genetic predisposition. Examples of modifiable risk factors include smoking, alcohol consumption, physical inactivity, and unhealthy diet, while non-modifiable risk factors include age, sex, and family history. It is important to note that having a risk factor does not guarantee that a person will develop the disease, but rather indicates an increased susceptibility.

A case-control study is an observational research design used to identify risk factors or causes of a disease or health outcome. In this type of study, individuals with the disease or condition (cases) are compared with similar individuals who do not have the disease or condition (controls). The exposure history or other characteristics of interest are then compared between the two groups to determine if there is an association between the exposure and the disease.

Case-control studies are often used when it is not feasible or ethical to conduct a randomized controlled trial, as they can provide valuable insights into potential causes of diseases or health outcomes in a relatively short period of time and at a lower cost than other study designs. However, because case-control studies rely on retrospective data collection, they are subject to biases such as recall bias and selection bias, which can affect the validity of the results. Therefore, it is important to carefully design and conduct case-control studies to minimize these potential sources of bias.

Discriminant analysis is a statistical method used for classifying observations or individuals into distinct categories or groups based on multiple predictor variables. It is commonly used in medical research to help diagnose or predict the presence or absence of a particular condition or disease.

In discriminant analysis, a linear combination of the predictor variables is created, and the resulting function is used to determine the group membership of each observation. The function is derived from the means and variances of the predictor variables for each group, with the goal of maximizing the separation between the groups while minimizing the overlap.

There are two types of discriminant analysis:

1. Linear Discriminant Analysis (LDA): This method assumes that the predictor variables are normally distributed and have equal variances within each group. LDA is used when there are two or more groups to be distinguished.
2. Quadratic Discriminant Analysis (QDA): This method does not assume equal variances within each group, allowing for more flexibility in modeling the distribution of predictor variables. QDA is used when there are two or more groups to be distinguished.

Discriminant analysis can be useful in medical research for developing diagnostic models that can accurately classify patients based on a set of clinical or laboratory measures. It can also be used to identify which predictor variables are most important in distinguishing between different groups, providing insights into the underlying biological mechanisms of disease.

A computer simulation is a process that involves creating a model of a real-world system or phenomenon on a computer and then using that model to run experiments and make predictions about how the system will behave under different conditions. In the medical field, computer simulations are used for a variety of purposes, including:

1. Training and education: Computer simulations can be used to create realistic virtual environments where medical students and professionals can practice their skills and learn new procedures without risk to actual patients. For example, surgeons may use simulation software to practice complex surgical techniques before performing them on real patients.
2. Research and development: Computer simulations can help medical researchers study the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone. By creating detailed models of cells, tissues, organs, or even entire organisms, researchers can use simulation software to explore how these systems function and how they respond to different stimuli.
3. Drug discovery and development: Computer simulations are an essential tool in modern drug discovery and development. By modeling the behavior of drugs at a molecular level, researchers can predict how they will interact with their targets in the body and identify potential side effects or toxicities. This information can help guide the design of new drugs and reduce the need for expensive and time-consuming clinical trials.
4. Personalized medicine: Computer simulations can be used to create personalized models of individual patients based on their unique genetic, physiological, and environmental characteristics. These models can then be used to predict how a patient will respond to different treatments and identify the most effective therapy for their specific condition.

Overall, computer simulations are a powerful tool in modern medicine, enabling researchers and clinicians to study complex systems and make predictions about how they will behave under a wide range of conditions. By providing insights into the behavior of biological systems at a level of detail that would be difficult or impossible to achieve through experimental methods alone, computer simulations are helping to advance our understanding of human health and disease.

In the field of medicine, "time factors" refer to the duration of symptoms or time elapsed since the onset of a medical condition, which can have significant implications for diagnosis and treatment. Understanding time factors is crucial in determining the progression of a disease, evaluating the effectiveness of treatments, and making critical decisions regarding patient care.

For example, in stroke management, "time is brain," meaning that rapid intervention within a specific time frame (usually within 4.5 hours) is essential to administering tissue plasminogen activator (tPA), a clot-busting drug that can minimize brain damage and improve patient outcomes. Similarly, in trauma care, the "golden hour" concept emphasizes the importance of providing definitive care within the first 60 minutes after injury to increase survival rates and reduce morbidity.

Time factors also play a role in monitoring the progression of chronic conditions like diabetes or heart disease, where regular follow-ups and assessments help determine appropriate treatment adjustments and prevent complications. In infectious diseases, time factors are crucial for initiating antibiotic therapy and identifying potential outbreaks to control their spread.

Overall, "time factors" encompass the significance of recognizing and acting promptly in various medical scenarios to optimize patient outcomes and provide effective care.

Diagnostic techniques in ophthalmology refer to the various methods and tests used by eye specialists (ophthalmologists) to examine, evaluate, and diagnose conditions related to the eyes and visual system. Here are some commonly used diagnostic techniques:

1. Visual Acuity Testing: This is a basic test to measure the sharpness of a person's vision. It typically involves reading letters or numbers from an eye chart at a specific distance.
2. Refraction Test: This test helps determine the correct lens prescription for glasses or contact lenses by measuring how light is bent as it passes through the cornea and lens.
3. Slit Lamp Examination: A slit lamp is a microscope that allows an ophthalmologist to examine the structures of the eye, including the cornea, iris, lens, and retina, in great detail.
4. Tonometry: This test measures the pressure inside the eye (intraocular pressure) to detect conditions like glaucoma. Common methods include applanation tonometry and non-contact tonometry.
5. Retinal Imaging: Several techniques are used to capture images of the retina, including fundus photography, fluorescein angiography, and optical coherence tomography (OCT). These tests help diagnose conditions like macular degeneration, diabetic retinopathy, and retinal detachments.
6. Color Vision Testing: This test evaluates a person's ability to distinguish between different colors, which can help detect color vision deficiencies or neurological disorders affecting the visual pathway.
7. Visual Field Testing: This test measures a person's peripheral (or side) vision and can help diagnose conditions like glaucoma, optic nerve damage, or brain injuries.
8. Pupillary Reactions Tests: These tests evaluate how the pupils respond to light and near objects, which can provide information about the condition of the eye's internal structures and the nervous system.
9. Ocular Motility Testing: This test assesses eye movements and alignment, helping diagnose conditions like strabismus (crossed eyes) or nystagmus (involuntary eye movement).
10. Corneal Topography: This non-invasive imaging technique maps the curvature of the cornea, which can help detect irregularities, assess the fit of contact lenses, and plan refractive surgery procedures.

Artificial Intelligence (AI) in the medical context refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction.

In healthcare, AI is increasingly being used to analyze large amounts of data, identify patterns, make decisions, and perform tasks that would normally require human intelligence. This can include tasks such as diagnosing diseases, recommending treatments, personalizing patient care, and improving clinical workflows.

Examples of AI in medicine include machine learning algorithms that analyze medical images to detect signs of disease, natural language processing tools that extract relevant information from electronic health records, and robot-assisted surgery systems that enable more precise and minimally invasive procedures.

Labor onset, also known as the start of labor, refers to the beginning of regular and coordinated uterine contractions that ultimately result in the delivery of a baby. This is usually marked by the presence of regular contractions that increase in intensity and frequency over time, along with cervical dilation and effacement (thinning and shortening of the cervix).

There are two types of labor onset: spontaneous and induced. Spontaneous labor onset occurs naturally, without any medical intervention, while induced labor onset is initiated by medical professionals using various methods such as medication or mechanical dilation of the cervix.

It's important to note that the onset of labor can be a challenging concept to define precisely, and different healthcare providers may use slightly different criteria to diagnose the start of labor.

Reference values, also known as reference ranges or reference intervals, are the set of values that are considered normal or typical for a particular population or group of people. These values are often used in laboratory tests to help interpret test results and determine whether a patient's value falls within the expected range.

The process of establishing reference values typically involves measuring a particular biomarker or parameter in a large, healthy population and then calculating the mean and standard deviation of the measurements. Based on these statistics, a range is established that includes a certain percentage of the population (often 95%) and excludes extreme outliers.

It's important to note that reference values can vary depending on factors such as age, sex, race, and other demographic characteristics. Therefore, it's essential to use reference values that are specific to the relevant population when interpreting laboratory test results. Additionally, reference values may change over time due to advances in measurement technology or changes in the population being studied.

Liver cirrhosis is a chronic, progressive disease characterized by the replacement of normal liver tissue with scarred (fibrotic) tissue, leading to loss of function. The scarring is caused by long-term damage from various sources such as hepatitis, alcohol abuse, nonalcoholic fatty liver disease, and other causes. As the disease advances, it can lead to complications like portal hypertension, fluid accumulation in the abdomen (ascites), impaired brain function (hepatic encephalopathy), and increased risk of liver cancer. It is generally irreversible, but early detection and treatment of underlying causes may help slow down its progression.

Decision support techniques are methods used to help individuals or groups make informed and effective decisions in a medical context. These techniques can involve various approaches, such as:

1. **Clinical Decision Support Systems (CDSS):** Computerized systems that provide clinicians with patient-specific information and evidence-based recommendations to assist in decision-making. CDSS can be integrated into electronic health records (EHRs) or standalone applications.

2. **Evidence-Based Medicine (EBM):** A systematic approach to clinical decision-making that involves the integration of best available research evidence, clinician expertise, and patient values and preferences. EBM emphasizes the importance of using high-quality scientific studies to inform medical decisions.

3. **Diagnostic Reasoning:** The process of formulating a diagnosis based on history, physical examination, and diagnostic tests. Diagnostic reasoning techniques may include pattern recognition, hypothetico-deductive reasoning, or a combination of both.

4. **Predictive Modeling:** The use of statistical models to predict patient outcomes based on historical data and clinical variables. These models can help clinicians identify high-risk patients and inform treatment decisions.

5. **Cost-Effectiveness Analysis (CEA):** An economic evaluation technique that compares the costs and benefits of different medical interventions to determine which option provides the most value for money. CEA can assist decision-makers in allocating resources efficiently.

6. **Multicriteria Decision Analysis (MCDA):** A structured approach to decision-making that involves identifying, evaluating, and comparing multiple criteria or objectives. MCDA can help clinicians and patients make complex decisions by accounting for various factors, such as efficacy, safety, cost, and patient preferences.

7. **Shared Decision-Making (SDM):** A collaborative approach to decision-making that involves the clinician and patient working together to choose the best course of action based on the available evidence, clinical expertise, and patient values and preferences. SDM aims to empower patients to participate actively in their care.

These techniques can be used individually or in combination to support medical decision-making and improve patient outcomes.

A Solitary Pulmonary Nodule (SPN) is a single, round or oval-shaped lung shadow that measures up to 3 cm in diameter on a chest radiograph. It is also known as a "coin lesion" due to its appearance. SPNs are usually discovered incidentally during routine chest X-rays or CT scans. They can be benign or malignant, and their nature is determined through further diagnostic tests such as PET scans, biopsies, or follow-up imaging studies.

Computer-assisted radiographic image interpretation is the use of computer algorithms and software to assist and enhance the interpretation and analysis of medical images produced by radiography, such as X-rays, CT scans, and MRI scans. The computer-assisted system can help identify and highlight certain features or anomalies in the image, such as tumors, fractures, or other abnormalities, which may be difficult for the human eye to detect. This technology can improve the accuracy and speed of diagnosis, and may also reduce the risk of human error. It's important to note that the final interpretation and diagnosis is always made by a qualified healthcare professional, such as a radiologist, who takes into account the computer-assisted analysis in conjunction with their clinical expertise and knowledge.

An immunoassay is a biochemical test that measures the presence or concentration of a specific protein, antibody, or antigen in a sample using the principles of antibody-antigen reactions. It is commonly used in clinical laboratories to diagnose and monitor various medical conditions such as infections, hormonal disorders, allergies, and cancer.

Immunoassays typically involve the use of labeled reagents, such as enzymes, radioisotopes, or fluorescent dyes, that bind specifically to the target molecule. The amount of label detected is proportional to the concentration of the target molecule in the sample, allowing for quantitative analysis.

There are several types of immunoassays, including enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), fluorescence immunoassay (FIA), and chemiluminescent immunoassay (CLIA). Each type has its own advantages and limitations, depending on the sensitivity, specificity, and throughput required for a particular application.

A cohort study is a type of observational study in which a group of individuals who share a common characteristic or exposure are followed up over time to determine the incidence of a specific outcome or outcomes. The cohort, or group, is defined based on the exposure status (e.g., exposed vs. unexposed) and then monitored prospectively to assess for the development of new health events or conditions.

Cohort studies can be either prospective or retrospective in design. In a prospective cohort study, participants are enrolled and followed forward in time from the beginning of the study. In contrast, in a retrospective cohort study, researchers identify a cohort that has already been assembled through medical records, insurance claims, or other sources and then look back in time to assess exposure status and health outcomes.

Cohort studies are useful for establishing causality between an exposure and an outcome because they allow researchers to observe the temporal relationship between the two. They can also provide information on the incidence of a disease or condition in different populations, which can be used to inform public health policy and interventions. However, cohort studies can be expensive and time-consuming to conduct, and they may be subject to bias if participants are not representative of the population or if there is loss to follow-up.

A cross-sectional study is a type of observational research design that examines the relationship between variables at one point in time. It provides a snapshot or a "cross-section" of the population at a particular moment, allowing researchers to estimate the prevalence of a disease or condition and identify potential risk factors or associations.

In a cross-sectional study, data is collected from a sample of participants at a single time point, and the variables of interest are measured simultaneously. This design can be used to investigate the association between exposure and outcome, but it cannot establish causality because it does not follow changes over time.

Cross-sectional studies can be conducted using various data collection methods, such as surveys, interviews, or medical examinations. They are often used in epidemiology to estimate the prevalence of a disease or condition in a population and to identify potential risk factors that may contribute to its development. However, because cross-sectional studies only provide a snapshot of the population at one point in time, they cannot account for changes over time or determine whether exposure preceded the outcome.

Therefore, while cross-sectional studies can be useful for generating hypotheses and identifying potential associations between variables, further research using other study designs, such as cohort or case-control studies, is necessary to establish causality and confirm any findings.

Image enhancement in the medical context refers to the process of improving the quality and clarity of medical images, such as X-rays, CT scans, MRI scans, or ultrasound images, to aid in the diagnosis and treatment of medical conditions. Image enhancement techniques may include adjusting contrast, brightness, or sharpness; removing noise or artifacts; or applying specialized algorithms to highlight specific features or structures within the image.

The goal of image enhancement is to provide clinicians with more accurate and detailed information about a patient's anatomy or physiology, which can help inform medical decision-making and improve patient outcomes.

Multivariate analysis is a statistical method used to examine the relationship between multiple independent variables and a dependent variable. It allows for the simultaneous examination of the effects of two or more independent variables on an outcome, while controlling for the effects of other variables in the model. This technique can be used to identify patterns, associations, and interactions among multiple variables, and is commonly used in medical research to understand complex health outcomes and disease processes. Examples of multivariate analysis methods include multiple regression, factor analysis, cluster analysis, and discriminant analysis.

Treatment outcome is a term used to describe the result or effect of medical treatment on a patient's health status. It can be measured in various ways, such as through symptoms improvement, disease remission, reduced disability, improved quality of life, or survival rates. The treatment outcome helps healthcare providers evaluate the effectiveness of a particular treatment plan and make informed decisions about future care. It is also used in clinical research to compare the efficacy of different treatments and improve patient care.

An Enzyme-Linked Immunosorbent Assay (ELISA) is a type of analytical biochemistry assay used to detect and quantify the presence of a substance, typically a protein or peptide, in a liquid sample. It takes its name from the enzyme-linked antibodies used in the assay.

In an ELISA, the sample is added to a well containing a surface that has been treated to capture the target substance. If the target substance is present in the sample, it will bind to the surface. Next, an enzyme-linked antibody specific to the target substance is added. This antibody will bind to the captured target substance if it is present. After washing away any unbound material, a substrate for the enzyme is added. If the enzyme is present due to its linkage to the antibody, it will catalyze a reaction that produces a detectable signal, such as a color change or fluorescence. The intensity of this signal is proportional to the amount of target substance present in the sample, allowing for quantification.

ELISAs are widely used in research and clinical settings to detect and measure various substances, including hormones, viruses, and bacteria. They offer high sensitivity, specificity, and reproducibility, making them a reliable choice for many applications.

In the context of medicine and medical devices, calibration refers to the process of checking, adjusting, or confirming the accuracy of a measurement instrument or system. This is typically done by comparing the measurements taken by the device being calibrated to those taken by a reference standard of known accuracy. The goal of calibration is to ensure that the medical device is providing accurate and reliable measurements, which is critical for making proper diagnoses and delivering effective treatment. Regular calibration is an important part of quality assurance and helps to maintain the overall performance and safety of medical devices.

Optic nerve diseases refer to a group of conditions that affect the optic nerve, which transmits visual information from the eye to the brain. These diseases can cause various symptoms such as vision loss, decreased visual acuity, changes in color vision, and visual field defects. Examples of optic nerve diseases include optic neuritis (inflammation of the optic nerve), glaucoma (damage to the optic nerve due to high eye pressure), optic nerve damage from trauma or injury, ischemic optic neuropathy (lack of blood flow to the optic nerve), and optic nerve tumors. Treatment for optic nerve diseases varies depending on the specific condition and may include medications, surgery, or lifestyle changes.

X-ray computed tomography (CT or CAT scan) is a medical imaging method that uses computer-processed combinations of many X-ray images taken from different angles to produce cross-sectional (tomographic) images (virtual "slices") of the body. These cross-sectional images can then be used to display detailed internal views of organs, bones, and soft tissues in the body.

The term "computed tomography" is used instead of "CT scan" or "CAT scan" because the machines take a series of X-ray measurements from different angles around the body and then use a computer to process these data to create detailed images of internal structures within the body.

CT scanning is a noninvasive, painless medical test that helps physicians diagnose and treat medical conditions. CT imaging provides detailed information about many types of tissue including lung, bone, soft tissue and blood vessels. CT examinations can be performed on every part of the body for a variety of reasons including diagnosis, surgical planning, and monitoring of therapeutic responses.

In computed tomography (CT), an X-ray source and detector rotate around the patient, measuring the X-ray attenuation at many different angles. A computer uses this data to construct a cross-sectional image by the process of reconstruction. This technique is called "tomography". The term "computed" refers to the use of a computer to reconstruct the images.

CT has become an important tool in medical imaging and diagnosis, allowing radiologists and other physicians to view detailed internal images of the body. It can help identify many different medical conditions including cancer, heart disease, lung nodules, liver tumors, and internal injuries from trauma. CT is also commonly used for guiding biopsies and other minimally invasive procedures.

In summary, X-ray computed tomography (CT or CAT scan) is a medical imaging technique that uses computer-processed combinations of many X-ray images taken from different angles to produce cross-sectional images of the body. It provides detailed internal views of organs, bones, and soft tissues in the body, allowing physicians to diagnose and treat medical conditions.

Nephelometry and turbidimetry are methods used in clinical laboratories to measure the amount of particles, such as proteins or cells, present in a liquid sample. The main difference between these two techniques lies in how they detect and quantify the particles.

1. Nephelometry: This is a laboratory method that measures the amount of light scattered by suspended particles in a liquid medium at a 90-degree angle to the path of the incident light. When light passes through a sample containing particles, some of the light is absorbed, while some is scattered in various directions. In nephelometry, a light beam is shone into the sample, and a detector measures the intensity of the scattered light at a right angle to the light source. The more particles present in the sample, the higher the intensity of scattered light, which correlates with the concentration of particles in the sample. Nephelometry is often used to measure the levels of immunoglobulins, complement components, and other proteins in serum or plasma.

2. Turbidimetry: This is another laboratory method that measures the amount of light blocked or absorbed by suspended particles in a liquid medium. In turbidimetry, a light beam is shone through the sample, and the intensity of the transmitted light is measured. The more particles present in the sample, the more light is absorbed or scattered, resulting in lower transmitted light intensity. Turbidimetric measurements are typically reported as percent transmittance, which is the ratio of the intensity of transmitted light to that of the incident light expressed as a percentage. Turbidimetry can be used to measure various substances, such as proteins, cells, and crystals, in body fluids like urine, serum, or plasma.

In summary, nephelometry measures the amount of scattered light at a 90-degree angle, while turbidimetry quantifies the reduction in transmitted light intensity due to particle presence. Both methods are useful for determining the concentration of particles in liquid samples and are commonly used in clinical laboratories for diagnostic purposes.

Reference standards in a medical context refer to the established and widely accepted norms or benchmarks used to compare, evaluate, or measure the performance, accuracy, or effectiveness of diagnostic tests, treatments, or procedures. These standards are often based on extensive research, clinical trials, and expert consensus, and they help ensure that healthcare practices meet certain quality and safety thresholds.

For example, in laboratory medicine, reference standards may consist of well-characterized samples with known concentrations of analytes (such as chemicals or biological markers) that are used to calibrate instruments and validate testing methods. In clinical practice, reference standards may take the form of evidence-based guidelines or best practices that define appropriate care for specific conditions or patient populations.

By adhering to these reference standards, healthcare professionals can help minimize variability in test results, reduce errors, improve diagnostic accuracy, and ensure that patients receive consistent, high-quality care.

Support Vector Machines (SVM) is not a medical term, but a concept in machine learning, a branch of artificial intelligence. SVM is used in various fields including medicine for data analysis and pattern recognition. Here's a brief explanation of SVM:

Support Vector Machines is a supervised learning algorithm which analyzes data and recognizes patterns, used for classification and regression analysis. The goal of SVM is to find the optimal boundary or hyperplane that separates data into different classes with the maximum margin. This margin is the distance between the hyperplane and the nearest data points, also known as support vectors. By finding this optimal boundary, SVM can effectively classify new data points.

In the context of medical research, SVM has been used for various applications such as:

* Classifying medical images (e.g., distinguishing between cancerous and non-cancerous tissues)
* Predicting patient outcomes based on clinical or genetic data
* Identifying biomarkers associated with diseases
* Analyzing electronic health records to predict disease risk or treatment response

Therefore, while SVM is not a medical term per se, it is an important tool in the field of medical informatics and bioinformatics.

Brain Natriuretic Peptide (BNP) is a type of natriuretic peptide that is primarily produced in the heart, particularly in the ventricles. Although it was initially identified in the brain, hence its name, it is now known that the cardiac ventricles are the main source of BNP in the body.

BNP is released into the bloodstream in response to increased stretching or distension of the heart muscle cells due to conditions such as heart failure, hypertension, and myocardial infarction (heart attack). Once released, BNP binds to specific receptors in the kidneys, causing an increase in urine production and excretion of sodium, which helps reduce fluid volume and decrease the workload on the heart.

BNP also acts as a hormone that regulates various physiological functions, including blood pressure, cardiac remodeling, and inflammation. Measuring BNP levels in the blood is a useful diagnostic tool for detecting and monitoring heart failure, as higher levels of BNP are associated with more severe heart dysfunction.

Cervical ripening is a medical term that refers to the process of softening, thinning, and dilating (opening) the cervix, which is the lower part of the uterus that opens into the vagina. This process typically occurs naturally in preparation for childbirth, as the body prepares for labor.

Cervical ripening can also be induced medically, using various methods such as prostaglandin gels or medications, or mechanical means such as a Foley catheter or dilators. These interventions are used to help prepare the cervix for delivery in cases where labor is not progressing on its own or when there is a medical indication to induce labor.

It's important to note that cervical ripening is different from labor induction, which involves stimulating uterine contractions to begin or strengthen labor. Cervical ripening may be a necessary step before labor induction can occur.

In the context of medicine and healthcare, 'probability' does not have a specific medical definition. However, in general terms, probability is a branch of mathematics that deals with the study of numerical quantities called probabilities, which are assigned to events or sets of events. Probability is a measure of the likelihood that an event will occur. It is usually expressed as a number between 0 and 1, where 0 indicates that the event is impossible and 1 indicates that the event is certain to occur.

In medical research and statistics, probability is often used to quantify the uncertainty associated with statistical estimates or hypotheses. For example, a p-value is a probability that measures the strength of evidence against a hypothesis. A small p-value (typically less than 0.05) suggests that the observed data are unlikely under the assumption of the null hypothesis, and therefore provides evidence in favor of an alternative hypothesis.

Probability theory is also used to model complex systems and processes in medicine, such as disease transmission dynamics or the effectiveness of medical interventions. By quantifying the uncertainty associated with these models, researchers can make more informed decisions about healthcare policies and practices.

Medical mass screening, also known as population screening, is a public health service that aims to identify and detect asymptomatic individuals in a given population who have or are at risk of a specific disease. The goal is to provide early treatment, reduce morbidity and mortality, and prevent the spread of diseases within the community.

A mass screening program typically involves offering a simple, quick, and non-invasive test to a large number of people in a defined population, regardless of their risk factors or symptoms. Those who test positive are then referred for further diagnostic tests and appropriate medical interventions. Examples of mass screening programs include mammography for breast cancer detection, PSA (prostate-specific antigen) testing for prostate cancer, and fecal occult blood testing for colorectal cancer.

It is important to note that mass screening programs should be evidence-based, cost-effective, and ethically sound, with clear benefits outweighing potential harms. They should also consider factors such as the prevalence of the disease in the population, the accuracy and reliability of the screening test, and the availability and effectiveness of treatment options.

Early diagnosis refers to the identification and detection of a medical condition or disease in its initial stages, before the appearance of significant symptoms or complications. This is typically accomplished through various screening methods, such as medical history reviews, physical examinations, laboratory tests, and imaging studies. Early diagnosis can allow for more effective treatment interventions, potentially improving outcomes and quality of life for patients, while also reducing the overall burden on healthcare systems.

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Procedures for method evaluation and method comparison include ROC curve analysis, Bland-Altman plot, as well as Deming and ... ISBN 978-0-4298-7787-2. Krzanowski, Wojtek J.; Hand, David J. (2009). ROC Curves for Continuous Data. Boca Raton, FL: Chapman ...
As a rule of thumb, the fewer the biomarkers that one uses to maximize the AUC of the ROC curve, the better. ROCCET's ROC curve ... An image of different ROC curves is shown in Figure 1. ROC curves provide a simple visual method for one to determine the ... ROC) curve. ROC curves plot the sensitivity of a biomarker on the y axis, against the false discovery rate (1- specificity) on ... The AUC (area under the curve) of the ROC curve reflects the overall accuracy and the separation performance of the biomarker ( ...
Another useful single measure is "area under the ROC curve", AUC. An F-score is a combination of the precision and the recall, ... ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both ( ... Chicco D.; Jurman G. (2023). "The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for ... Powers, David M. W. (2011). "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation". ...
Evaluation = Confusion Matrix, Risk Charts, Cost Curve, Hand, Lift, ROC, Precision, Sensitivity. Charts = Box Plot, Histogram, ...
The value a can be used to plot a summary ROC (SROC) curve. Consider a test with the following 2×2 confusion matrix: We ... Moses, L. E.; Shapiro, D; Littenberg, B (1993). "Combining independent studies of a diagnostic test into a summary ROC curve: ...
The value of the EER can be easily obtained from the ROC curve. The EER is a quick way to compare the accuracy of devices with ... Receiver operating characteristic or relative operating characteristic (ROC): The ROC plot is a visual characterization of the ... different ROC curves. In general, the device with the lowest EER is the most accurate. Failure to enroll rate (FTE or FER): the ...
It achieved an area under the ROC (Receiver Operating Characteristic) curve of 0.89. To provide explain-ability, they developed ...
... a coherent alternative to the area under the ROC curve. Machine Learning, 77, 103-123 Hand D.J. (2018) Statistical challenges ... A coherent alternative to the area under the ROC curve". Machine Learning. 77: 103-123. doi:10.1007/s10994-009-5119-5. S2CID ...
It is common to report the area under the curve (AUC) to summarize a TOC or ROC curve. However, condensing diagnostic ability ... of the AUC is consistent for the same data whether you are calculating the area under the curve for a TOC curve or a ROC curve ... The curve shows accurate diagnosis of presence until the curve reaches a threshold of 86. The curve then levels off and ... At any given point in the ROC curve, it is possible to glean values for the ratios of false alarms/(false alarms+correct ...
... such as the area under the ROC-curve. Bias is the extent to which one response is more probable than another, averaging across ...
Includes a tool for grading and generating ROC curves from resultant sam files. Open-source, written in pure Java; supports all ...
"An experimental comparison of cross-validation techniques for estimating the area under the ROC curve". Computational ... as leave-pair-out cross-validation has been recommended as a nearly unbiased method for estimating the area under ROC curve of ...
This is the same as the area under the curve (AUC) for the ROC curve. A statistic called ρ that is linearly related to U and ... Hand, David J.; Till, Robert J. (2001). "A Simple Generalisation of the Area Under the ROC Curve for Multiple Class ... The U statistic is related to the area under the receiver operating characteristic curve[citation needed] (AUC). A U C 1 = U 1 ... Boston University (SPH), 2017 Fawcett, Tom (2006); An introduction to ROC analysis, Pattern Recognition Letters, 27, 861-874. ...
Several statistical methods may be used to evaluate the algorithm, such as ROC curves. If the learned patterns do not meet the ...
The area under the receiver operating characteristic (ROC) curve is widely used to evaluate its performance. Resulting hits ...
ROC) curve and its diagonal. It is related to the AUC (Area Under the ROC Curve) measure of performance given by A U C = ( G + ... states by Gini coefficient Lorenz curve Matthew effect Pareto distribution ROC analysis Suits index The Elephant Curve Utopia ... Hand, David J.; Till, Robert J. (2001). "A Simple Generalisation of the Area Under the ROC Curve for Multiple Class ... The Gini coefficient is usually defined mathematically based on the Lorenz curve, which plots the proportion of the total ...
Bradley, Andrew P (1997). "The use of the area under the ROC curve in the evaluation of machine learning algorithms" (PDF). ... "The Learning Curve Method Applied to Clustering." AISTATS. 2001. Fanaee-T, Hadi; Gama, Joao (2013). "Event labeling combining ... Kudo, Mineichi; Toyama, Jun; Shimbo, Masaru (1999). "Multidimensional curve classification using passing-through regions". ...
More exotic fitness functions that explore model granularity include the area under the ROC curve and rank measure. Also ...
the area between the full ROC curve and the triangular ROC curve including only (0,0), (1,1) and one selected operating point ... Sometimes, the ROC is used to generate a summary statistic. Common versions are: the intercept of the ROC curve with the line ... Under these assumptions, the shape of the ROC is entirely determined by d′. However, any attempt to summarize the ROC curve ... Several studies criticize the usage of the ROC curve and its area under the curve as measurements for assessing binary ...
Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med ... This is not the case for other metrics such as area-under-the-curve, Brier score or net benefit. PredictABEL: an R package for ... Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928-935. Pencina MJ ...
One quantitative measure is a receiver operating characteristic (ROC) curve, which measures the tradeoff between false ... Ideally, there should be a high probability of detection with few false positives, but such curves have not been obtained for ...
... area under curve and precision/recall curve. The parametrization can be visualized by coloring the curve according to cutoff. ... ROCR: The ROCR is an R package for evaluating and visualizing classifier performance . It is a flexible tool for creating ROC ... It includes a function, AUC, to calculate area under the curve. It also includes functions for half-life estimation for a ... between the dosing regimen and the body's exposure to the drug as measured by the nonlinear concentration time curve. ...
The output is called a CAP curve. The CAP is distinct from the receiver operating characteristic (ROC) curve, which plots the ... The cumulative accuracy profile (CAP) and ROC curve are both commonly used by banks and regulators to analyze the ... and a randomized curve. A good model will have a CAP between the perfect and random curves; the closer a model is to the ... A cumulative accuracy profile can be used to evaluate a model by comparing the current curve to both the 'perfect' ...
AUC-ROC The area under the receiver operating characteristic curve (AUC-ROC) reflects the relationship between sensitivity and ... Cutoff values for positive and negative tests can influence specificity and sensitivity, but they do not affect AUC-ROC. Number ... High-quality tests will have an AUC-ROC approaching 1, and high-quality publications about clinical tests will provide ...
... is the area under the ROC curve (AUC). Some example results of PGS performance, as measured in AUC (0 ≤ AUC ≤ 1 where a larger ...
When the features represent distinguishable patterns of burst and suppression, a fixed threshold using ROC-curve or machine ...
The image below shows an ROC curve, measuring the probability of detection over the probability of false detection, as well as ...
ROC curves are commonly drawn to show sensitivity as a function of false positive rate for a given detection confidence and ... ROC). These parameters are sensitivity, probability of correct detection, false positive rate and response time. Ideally, the ...
More recently, receiver operating characteristic (ROC) curves have been used to evaluate the tradeoff between true- and false- ...
... the ROC curve of C a {\displaystyle C_{a}} is never above the ROC curve of C b {\displaystyle C_{b}} the ROC curve of C a {\ ... The AUC is simply defined as the area of the ROC space that lies below the ROC curve. However, in the ROC space there are ... displaystyle C_{a}} is never below the ROC curve of C b {\displaystyle C_{b}} the classifiers ROC curves cross each other. ... Thus, the partial AUC was computed as the area under the ROC curve in the vertical band of the ROC space where FPR is in the ...
... From. Roger Newson ,[email protected],. To. [email protected]. Subject. ... I have used Robert Centors ROC analyzer for calculating the non-parametric ROC area of even binary diagnostic values. The ROC ... st: Re: ROC curve for ordinal data. Date. Fri, 19 Dec 2003 18:24:01 +0000. At 19:05 19/12/03 +0100, Roland Andersson wrote: ... The Area under an ROC Curve with Limited Information Wilbert B. van den Hout Another reference, which explains why the ROC area ...
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Prism provides in the Classification and Interpolation section of options for simple logistic regression is to generate an ROC ... section of options for simple logistic regression is to generate an ROC curve and to calculate the area under this curve (AUC ... Learn more about interpreting AUC. The results for the area under the ROC curve that Prism reports include: ... curve and to calculate the area under... ...
... orders may be available on multiple receiver operating characteristic curves. For example, being closer to delivery, fetal ... Estimation of multiple ordered ROC curves using placement values Soutik Ghosal 1 , Katherine L Grantz 2 , Zhen Chen 1 ... Estimation of multiple ordered ROC curves using placement values Soutik Ghosal et al. Stat Methods Med Res. 2022 Aug. ... A note on modeling placement values in the analysis of receiver operating characteristic curves. Chen Z, Ghosal S. Chen Z, et ...
Survival model predictive accuracy and ROC curves Patrick J Heagerty et al. Biometrics. 2005 Mar. ... Survival model predictive accuracy and ROC curves Patrick J Heagerty 1 , Yingye Zheng ... ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are ... Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) ...
GeneXproTools Knowledge Base Area Under the ROC Curve Fitness Function ... ROC Analysis See Also:. Measures of Fit for Regression. Measures of Fit for Classification. Measures of Fit for Logistic ...
Area under the ROC curve when there is imbalance: is there a problem, and if not, why does this rumor exist? ... I am a little bit confused about the Area Under Curve (AUC) of ROC and the overall accuracy. ... ROC. and accuracy. are fundamentally two different concepts.. Generally speaking, ROC. describes the discriminative power of a ... In practice, the ROC can give us more information, and we would like to choose the classier case by case. For example, the spam ...
ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are ...
... roc_null = roc(y, x_null, direction=,, quiet=TRUE) roc_alt = roc(y, x_alt, direction=,, quiet=TRUE) AUC_null[i] = auc(roc_ ... roc_null)) sd_alt[i] = sqrt(var(roc_alt)) crit = qnorm(1-0.05/2, mean=auc(roc_null), sd=sqrt(var(roc_null))) power[i] = 1 - ... I have tried using the power.roc.test function from pROC package on R, but realise that it is meant for paired ROC curves only ... One way is to get the variances of ROC curves by pROC::var. . Followed by calculating the critical value of null hypothesis roc ...
ROC Curves. The discriminatory ability of PSI, CURB-65 and APACHE-II scores to predict in-hospital mortality and 60-day ... mortality of COPD-CAP patients were analyzed and compared using areas under receiver operating characteristic (ROCs) curves ( ...
Click the icon to learn about the relationship between AUC and ROC curves. AUC represents the area under an ROC curve. For ... Conversely, the ROC curve for a classifier that cant separate classes at all is as follows. The area of this gray region is ... A loss curve plots training loss vs. the number of iterations. A loss curve provides the following hints about training:. *A ... The shape of an ROC curve suggests a binary classification models ability to separate positive classes from negative classes. ...
Second axis shows the ROC curves ax2 = fig.add_subplot(122) for name, y_prob in zip(names, probs): fpr, tpr, thresholds = roc_ ... Star/Quasar Classification ROC Curves¶. Figure 9.18. The left panel shows data used in color-based photometric classification ... The right panel shows ROC curves for quasar identification based on u - g , g - r , r - i , and i - z colors. Labels are the ... curve(y_test, y_prob) fpr = np.concatenate([[0], fpr]) tpr = np.concatenate([[0], tpr]) ax2.plot(fpr, tpr, label=labels[name]) ...
cat( export_interactive_roc(basicplot, prefix = a) ). .tess { fill: blue; stroke: blue; stroke-width: 0px; opacity: 0; } Inf ... The Roc Geom. Next I use the ggplot. function to define the aesthetics, and the geom_roc. function to add an ROC curve layer. ... Generate ROC Curve Charts for Print and Interactive Use. Michael C Sachs. 2023-10-06. Introduction. About ROC Curves. The ... stat_roc. and geom_roc. are linked by default, with the stat doing the underlying computation of the empirical ROC curve, and ...
4:19) Now lets talk about the ROC curve that you see here in the upper left. So, what is an ROC curve? It is a plot of the ... ROC curves and Area Under the Curve explained (video). While competing in a Kaggle competition this summer, I came across a ... 0:00) This video should help you to gain an intuitive understanding of ROC curves and Area Under the Curve, also known as AUC. ... That means if you have three classes, you would create three ROC curves. In the first curve, you would choose the first class ...
The ROC analysis was done to calculate the area under the curve (Table 3; Figure 3). Comparative analysis of two culturing ... Table 3 Area Under the Curve. Test Result Variable(s). Area. Std. Errora a Under the nonparametric assumption; Asymptotic Sig.b ... ROC) curve, specificity, sensitivity, negative predictive value (NPV), positive predictive value (PPV) was calculated to ... diagnostic accuracy was calculated by ROC curve; p value ,0.05 was significant; -ve = Negative; +ve = Positive. ...
MultiClassROC: ROC Curves for Multi-Class Analysis. Function multiroc() can be used for computing and visualizing Receiver ... Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. It supports both One-vs ...
The area under the receiver operating characteristic curve was 0.778 in predicting unstable plaques. Conclusions The serum ... 3.6 ROC curve analysis. The optimal cutoff point for the serum level of RBP4 to predict the occurrence of unstable plaques in ... Receiver operating characteristic (ROC) curve was used to assess the best cutoff point for RBP4 to predict the presence of ... ROC curve analysis of RBP4 (green line) and 8-iso-PGF2α (black line) as markers for diagnosing unstable carotid plaques. ...
ROC curve evaluation is rapidly becoming a commonly used evaluation metric in machine learning, although evaluating ROC curves ... Researchers in the medical field have long been using ROC curves and have many well-studied methods for analyzing such curves, ... In this paper we study techniques for generating and evaluating confidence bands on ROC curves. ... has thus far been limited to studying the area under the curve (AUC) or generation of one-dimensional confidence intervals by ...
... curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all obs ... The STONE curve has several similarities with the ROC curve - plotting probability of detection against probability of false ... curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a ... The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples ...
Explain how a ROC Curve works?. Ans: A ROC curve is a graph showing the performance of a classification model at different ... The closer the curve to a 45 degree diagonal of ROC Space, the less accurate the test is. ... It is useful for measuring recall, precision, AUC-ROC curve, and accuracy. The diagonal of the matrix contains all the true or ... The closer the curve follows the left-hand border and then the top border, the more accurate the test is. ...
Area Under the Curve (AUC) , 0.85), and even higher when using protein ratios (AUC up to 0.95), that include some protein pairs ... curve analyses distinguish the plasma proteomes of ME/CFS patients from controls with a high degree of accuracy ( ... High Levels of Prediction Are Achieved Using Univariate ROC Curve Analysis. A receiver operating characteristic (ROC) curve ... The corresponding ROC curves are paired with each box plot and include the optimal cutoff (in red) along with the area under ...
Home/Acronym/AUC-ROC. AUC-ROC. Area Under the Receiver Operating Characteristic Curve. AUC-ROC is the acronym for Area Under ... The AUC-ROC is the area under this ROC curve. It ranges from 0 to 1, where a higher value indicates better model performance. ... The ROC curve is a plot that illustrates the true positive rate (sensitivity) against the false positive rate (1-specificity) ... An AUC-ROC of 1 represents a perfect classifier that can distinguish between the two classes without error, while an AUC-ROC of ...
ROC Curves. Yes. Yes. Yes. Yes. Signal Processing. Yes. Simultaneous Equations. Yes. Yes. Limited. Yes. ... Learning Curve. Data Manipulation. Statistical Analysis. Graphics. Specialties. Epi Info™. Both. Menus & Syntax. Gradual. ... Normality refers to the distribution of the values (e.g., the shape of a normal bell curve). The distribution is a summary of ...
ROC Curve * Real-Time Polymerase Chain Reaction * Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization ...
... with an area-under-the-curve of 0.998. With its high accuracy, this mobile and cost-effective method has the potential to be ... 3: ROC curve.. Demonstration of the false positive rate versus the true positive rate for our sickle cell detection framework. ... b ROC curves for various simulated blood smear areas. These plots (except the 1.25 mm2 one, which is our experimental result) ... Figure 4b also reports how the ROC curves are impacted as a function of the number of cells being screened per patient slide, ...
This mapping is called the receiver operating characteristic (ROC) curve (see Box A for details). The area under the curve (AUC ... The solid red line depicts the ROC curve for the credit-to-GDP gap based on all the available data in our sample. We can see ... One picks the part of the ROC curve that identifies a prediction rate of at least 66% of crises - here the only possible one is ... Evaluating EWIs: ROC curves, noise-to-signal ratios and critical thresholds. Selecting an early warning indicator (EWI) ...
Another curve that is examined when evaluating a machine learning model is the ROC curve. (ROC is short for "receiver operating ... When developing our models, we look to see how the precision-recall curve, the ROC curve, and the AUC change. ... Computing the full production precision-recall or ROC curve is thus more involved than computing the validation curves because ... Precision-recall and ROC curves. The next natural question is what good values are for the precision, recall, and false ...
  • In many diagnostic accuracy studies, a priori orders may be available on multiple receiver operating characteristic curves. (nih.gov)
  • Such an a priori order should be incorporated in estimating receiver operating characteristic curves and associated summary accuracy statistics, as it can potentially improve statistical efficiency of these estimates. (nih.gov)
  • We instead propose a new strategy that incorporates the order directly through the modeling of receiver operating characteristic curves. (nih.gov)
  • Receiver operating characteristic curves are a mainstay in binary classification and have seen widespread use from their inception characterizing radar receivers in 1941. (phmsociety.org)
  • Receiver-operating characteristic curves (ROC). (cdc.gov)
  • The accuracy of PSI was assessed using Receiver Operating Characteristic curves (ROC). (cdc.gov)
  • 2. A new parametric method based on S-distributions for computing receiver operating characteristic curves for continuous diagnostic tests. (nih.gov)
  • The STONE curve has several similarities with the ROC curve - plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero-intercept unity-slope line indicating better than random predictive ability. (essopenarchive.org)
  • One option Prism provides in the Classification and Interpolation section of options for simple logistic regression is to generate an ROC curve and to calculate the area under this curve (AUC). (graphpad.com)
  • This course presents more advanced statistical techniques such as Logistic regression, Diagnostic tests and ROC curves. (imperial.ac.uk)
  • How to calculate the AUC-ROC (Area Under the Curve - Receiver Operating Characteristic) for a logistic regression model in a statistics exam? (hireforstatisticsexam.com)
  • We present an AUC-ROC (Area Under the Curve - Receiver Operating Characteristic) study on a large-scale comparison of AUC-ROCs between a logistic regression model in relation to 3 common indicators: AUC-ROC, AUC-sensitivity, and AUC-specific area under the curve. (hireforstatisticsexam.com)
  • The results of the study provide an illustration of how a properly derived AUC-ROC estimate or the best overall AUC-ROC estimate can be generated in relation to both logistic and asymptotic models, using software routines and software analytics tools. (hireforstatisticsexam.com)
  • In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. (nih.gov)
  • Overall accuracy is based on one specific cutpoint, while ROC tries all of the cutpoint and plots the sensitivity and specificity. (stackexchange.com)
  • The ROC curve is a plot that illustrates the true positive rate (sensitivity) against the false positive rate (1-specificity) for different classification thresholds. (martech.zone)
  • The ROC (Receiver Operating Characteristic) curve analysis was used to assess the level of diagnostic accuracy through indexes of the Area below the curve (ABC), sensitivity (S) and specificity (E). The analysis was differentiated by gender and showed significant differences. (bvsalud.org)
  • Each point along a ROC represents the trade-off in sensitivity and specificity, depending on the threshold for an abnormal test. (cdc.gov)
  • The diagnostic test represented by the unbroken ROC curve is a better test than that represented by the broken ROC curve, as demonstrated by its greater sensitivity for any given specificity (and thus, greater area under the curve). (cdc.gov)
  • Did I just invent a Bayesian method for analysis of ROC curves? (stackexchange.com)
  • It is a standard practice to use a binormal model to obtain the ROC curve and the AUC, and Bayesian methods have been used. (intlpress.com)
  • 9. Bayesian bootstrap estimation of ROC curve. (nih.gov)
  • This observation led to evaluating the accuracy of classifications by computing performance metrics that consider only a specific region of interest (RoI) in the ROC space, rather than the whole space. (wikipedia.org)
  • These performance metrics are commonly known as "partial AUC" (pAUC): the pAUC is the area of the selected region of the ROC space that lies under the ROC curve. (wikipedia.org)
  • to, the precision recall characteristic curve, area under the curve metrics, bookmaker informedness and markedness. (phmsociety.org)
  • Receiver operating characteristic (ROC) curve analysis was used to reveal the potential capacity of the sReHo and dReHo metrics to distinguish IGDs from HCs. (biomedcentral.com)
  • 6. Transformation-invariant and nonparametric monotone smooth estimation of ROC curves. (nih.gov)
  • 11. Nonparametric estimation of ROC curves in the absence of a gold standard. (nih.gov)
  • 13. Minimum-norm estimation for binormal receiver operating characteristic (ROC) curves. (nih.gov)
  • 15. The "proper" binormal model: parametric receiver operating characteristic curve estimation with degenerate data. (nih.gov)
  • 17. Semi-parametric estimation of the binormal ROC curve for a continuous diagnostic test. (nih.gov)
  • The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. (phmsociety.org)
  • Shows or hides the ROC plot. (jmp.com)
  • The ROC plot is shown by default. (jmp.com)
  • If the response has two levels, the Lift curve plot displays a lift curve for the first level of the response only. (jmp.com)
  • If the response has more than two levels, the Lift curve plot displays a sub-outline of the curves for each response level. (jmp.com)
  • The Partial Area Under the ROC Curve (pAUC) is a metric for the performance of binary classifier. (wikipedia.org)
  • The area under the ROC curve (AUC) is often used to summarize in a single number the diagnostic ability of the classifier. (wikipedia.org)
  • The AUC is simply defined as the area of the ROC space that lies below the ROC curve. (wikipedia.org)
  • To overcome this limitation of AUC, it was proposed to compute the area under the ROC curve in the area of the ROC space that corresponds to interesting (i.e., practically viable or acceptable) values of FPR and TPR. (wikipedia.org)
  • I have used Robert Centors ROC analyzer for calculating the non-parametric ROC area of even binary diagnostic values. (stata.com)
  • The ROC area is useful when comparing the discriminating power of diagnostic variables independent of the incidence of the disease, even for binary variables. (stata.com)
  • Another reference, which explains why the ROC area is a good measure of predictive power for general continuous and discrete predictor variables, is my own Stata Journal article (Newson 2002). (stata.com)
  • I am a little bit confused about the Area Under Curve (AUC) of ROC and the overall accuracy. (stackexchange.com)
  • While competing in a Kaggle competition this summer, I came across a simple visualization (created by a fellow competitor) that helped me to gain a better intuitive understanding of ROC curves and Area Under the Curve (AUC). (dataschool.io)
  • 0:00 ) This video should help you to gain an intuitive understanding of ROC curves and Area Under the Curve, also known as AUC. (dataschool.io)
  • The area under the receiver operating characteristic curve was 0.778 in predicting unstable plaques. (degruyter.com)
  • Function multiroc() can be used for computing and visualizing Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. (r-project.org)
  • ROC curve evaluation is rapidly becoming a commonly used evaluation metric in machine learning, although evaluating ROC curves has thus far been limited to studying the area under the curve (AUC) or generation of one-dimensional confidence intervals by freezing one variable-the false-positive rate, or threshold on the classification scoring function. (fosterprovost.com)
  • AUC-ROC is the acronym for Area Under the Receiver Operating Characteristic Curve. (martech.zone)
  • We blindly tested this mobile sickle cell detection method using blood smears from 96 unique patients (including 32 SCD patients) that were imaged by our smartphone microscope, and achieved ~98% accuracy, with an area-under-the-curve of 0.998. (nature.com)
  • The use of the area under the roc curve in the evaluation of machine learning algorithms. (phmsociety.org)
  • Measuring classifier performance: A coherent alternative to the area under the roc curve. (phmsociety.org)
  • The predictive performance of these indexes was evaluated by calculating the area under the receiver operating characteristic curve. (nih.gov)
  • AUC just means Area under the curve. (analyticsvidhya.com)
  • A natural and popular way to compare two withdrawals is to use the receiver operating characteristic (ROC) curve and the area under the curve (AUC). (intlpress.com)
  • No significant difference existed in the area under the curve (AUC) for ROCs comparing B1200 (b = 1200 s/mm 2 ) to computed B2000 (c-B2000) in 5 readers. (nature.com)
  • Model selection was carried out using the Mathews Correlation Coefficient (MCC) and model performance was quantified in the validation set using MCC, the area under the precision/recall curve (AUPRC) and accuracy. (springer.com)
  • 10. Estimating the Area Under ROC Curve When the Fitted Binormal Curves Demonstrate Improper Shape. (nih.gov)
  • When we examined how well the model identified workers with clinically significant parkinsonism (UPDRS3≥15) the receiver operating characteristic area under the curve (AUC) was 0.72 (95% confidence interval [CI] 0.67, 0.77). (nih.gov)
  • A small simulation study shows that the skew-binormal model provides improved precision over the binormal model with similar AUCs but somewhat different ROC curves. (intlpress.com)
  • 1. The use of the 'binormal' model for parametric ROC analysis of quantitative diagnostic tests. (nih.gov)
  • 7. Equivalence of binormal likelihood-ratio and bi-chi-squared ROC curve models. (nih.gov)
  • It is computed based on the receiver operating characteristic (ROC) curve that illustrates the diagnostic ability of a given binary classifier system as its discrimination threshold is varied. (wikipedia.org)
  • In the ROC space, where x=FPR (false positive rate) and y=ROC(x)=TPR (true positive rate), it is A U C = ∫ x = 0 1 R O C ( x ) d x {\displaystyle AUC=\int _{x=0}^{1}ROC(x)\ dx} The AUC is widely used, especially for comparing the performances of two (or more) binary classifiers: the classifier that achieves the highest AUC is deemed better. (wikipedia.org)
  • An ROC curve is the most commonly used way to visualize the performance of a binary classifier , and AUC is (arguably) the best way to summarize its performance in a single number . (dataschool.io)
  • An ROC curve is a commonly used way to visualize the performance of a binary classifier , meaning a classifier with two possible output classes. (dataschool.io)
  • An AUC-ROC of 1 represents a perfect classifier that can distinguish between the two classes without error, while an AUC-ROC of 0.5 indicates that the model performs no better than random chance. (martech.zone)
  • Building on prior work the Prognostics and Health Management community naturally adopted ROC curves to visualize classifier performance. (phmsociety.org)
  • While the ROC curve is perhaps the best known visualization of binary classifier performance it is not the only game in town. (phmsociety.org)
  • It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. (essopenarchive.org)
  • However, when comparing two classifiers C a {\displaystyle C_{a}} and C b {\displaystyle C_{b}} , three situations are possible: the ROC curve of C a {\displaystyle C_{a}} is never above the ROC curve of C b {\displaystyle C_{b}} the ROC curve of C a {\displaystyle C_{a}} is never below the ROC curve of C b {\displaystyle C_{b}} the classifiers' ROC curves cross each other. (wikipedia.org)
  • when comparing two classifiers via the associated ROC curves, a relatively small change in selecting the RoI may lead to different conclusions: this happens when T P R 0 {\displaystyle TPR_{0}} is close to the point where the given ROC curves cross each other. (wikipedia.org)
  • ROC curve regression analysis: the use of ordinal regression models for diagnostic test assessment. (nih.gov)
  • An introduction to roc analysis. (phmsociety.org)
  • ROC curve analysis showed that the brain regions with altered sReHo and dReHo could distinguish individuals with IGD from HCs. (biomedcentral.com)
  • Like recent approaches to ROC curve analysis, we also incorporate a stochastic ordering. (intlpress.com)
  • Receiver operating characteristic (ROC) analysis and McNemar's test were performed to assess the relative performance of computed high b value DWI, native high b-value DWI and ADC maps. (nature.com)
  • Preliminary laboratory evaluations using the MedCalc™ ROC curve analysis software has been performed. (cdc.gov)
  • 3. A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method. (nih.gov)
  • 5. A comparison of parametric and nonparametric approaches to ROC analysis of quantitative diagnostic tests. (nih.gov)
  • 20. Bivariate random effects meta-analysis of ROC curves. (nih.gov)
  • The article contains an example of calculating confidence limits in Stata for the difference between 2 ROC areas for 2 different 'continuous' predictors and the same binary disease outcome. (stata.com)
  • The Receiver Operating Characteristic (ROC) curve is used to assess the accuracy of a continuous measurement for predicting a binary outcome. (opencpu.org)
  • Evaluate and/or summarize ROC or PR curves for feature selection. (github.io)
  • In the medical literature, ROC curves are commonly plotted without the cutoff values displayed. (opencpu.org)
  • Typically, such quantitative test results (eg, white blood cell count in cases of suspected bacterial pneumonia) follow some type of distribution curve (not necessarily a normal curve, although commonly depicted as such). (msdmanuals.com)
  • We achieve this by exploiting the link between placement value (the relative position of a diseased test score in the healthy score distribution), the cumulative distribution function of placement value, and receiver operating characteristic curve, and by building stochastically ordered random variables through mixture distributions. (nih.gov)
  • I would like to perform power and sample size calculations for comparison of unpaired receiver-operating characteristic (ROC) curves. (stackexchange.com)
  • The discriminatory ability of PSI, CURB-65 and APACHE-II scores to predict in-hospital mortality and 60-day mortality of COPD-CAP patients were analyzed and compared using areas under receiver operating characteristic (ROCs) curves ( Additional File 5: Figure S2 ). (medscape.com)
  • Several characteristic curves are then used to showcase the performance improvement of the physics informed condition indicator. (phmsociety.org)
  • ROC stands for Receiver Operator Characteristic (ROC). (analyticsvidhya.com)
  • 16. Advantages to transforming the receiver operating characteristic (ROC) curve into likelihood ratio co-ordinates. (nih.gov)
  • We use the Gibbs sampler to fit both models in order to estimate the ROC curves and the AUCs. (intlpress.com)
  • There are several ways in which to compute AUC-ROCs for an in vitro human trial. (hireforstatisticsexam.com)
  • In this paper, we aim to compute the AUC-ROC of the parameter Akaike information criterion (AICc) in a simple clinical statistics exam. (hireforstatisticsexam.com)
  • 14. Tests of equivalence and non-inferiority for diagnostic accuracy based on the paired areas under ROC curves. (nih.gov)
  • 18. A non-inferiority test for diagnostic accuracy based on the paired partial areas under ROC curves. (nih.gov)
  • The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. (wikipedia.org)
  • In other words, the pAUC is computed in the portion of the ROC space where the true positive rate is greater than a given threshold T P R 0 {\displaystyle TPR_{0}} (no upper limit is used, since it would not make sense to limit the number of true positives). (wikipedia.org)
  • A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. (essopenarchive.org)
  • For a particular threshold, if you want to calculate a ROC AUC Score , sklearn provides a function. (analyticsvidhya.com)
  • Use the lift curve to see whether you can correctly classify a large proportion of observations if you select only those with a fitted probability that exceeds a threshold value. (jmp.com)
  • However, in the ROC space there are regions where the values of FPR or TPR are unacceptable or not viable in practice. (wikipedia.org)
  • In practice, the ROC can give us more information, and we would like to choose the classier case by case. (stackexchange.com)
  • In practice, an AUC-ROC value closer to 1 is desirable, as it demonstrates the model's ability to accurately classify both positive and negative cases. (martech.zone)
  • begingroup$ ROC AUC is beneficial when the classes have different size. (stackexchange.com)
  • AUC (based on ROC) and overall accuracy seems not the same concept. (stackexchange.com)
  • I have tried using the power.roc.test function from pROC package on R, but realise that it is meant for paired ROC curves only. (stackexchange.com)
  • If you used validation, Lift curve is shown for each of the Training, Validation, and Test sets, if specified. (jmp.com)
  • As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). (dataschool.io)
  • The idea of the partial AUC was originally proposed with the goal of restricting the evaluation of given ROC curves in the range of false positive rates that are considered interesting for diagnostic purposes. (wikipedia.org)
  • In medicine, ROC curves have a long history of use for evaluating diagnostic tests in radiology and general diagnostics. (opencpu.org)
  • During the first 2 min after discontinuation of mechanical ventilation the following tests were performed: vital capacity, tidal volume, airway occlusion pressure (P(0.1)), minute ventilation, respiratory rate, maximal inspiratory pressure (MIP), respiratory frequency to tidal volume (f/V(T)), P(0.1)/MIP and P(0.1) x f/V(T). The areas under the curve showed that the tests had not the ability to distinguish between successful and unsuccessful weaning. (nih.gov)
  • layer includes the ROC curve line combined with points and labels to display the values of the biomarker at the different cutpoints. (opencpu.org)
  • Vector of values between 0 and 1 at which to evaluate the ROC or PR curve. (github.io)
  • A list of tibbles with x and y coordinate values for the ROC/PR curve for the given experimental replicate. (github.io)
  • Conclusions: Based on the ROC curve, PSI can accurately predict Unsustainable heat stress exposures (AUC 0.79). (cdc.gov)
  • This is exactly what the ROC curve is, \(FPF(c)\) on the \(x\) axis and \(TPF(c)\) along the \(y\) axis. (opencpu.org)
  • Other problems with ROC curve plots are abundant in the medical literature. (opencpu.org)
  • containing both identifying information and the feature selection curve results aggregated over experimental replicates. (github.io)
  • Widely used and accepted, the ROC curve is the default option for many application spaces. (phmsociety.org)
  • 12. Recent advances in observer performance methodology: jackknife free-response ROC (JAFROC). (nih.gov)
  • are linked by default, with the stat doing the underlying computation of the empirical ROC curve, and the geom consisting of the ROC curve layer. (opencpu.org)
  • Either "ROC" or "PR" indicating whether to evaluate the ROC or Precision-Recall curve. (github.io)
  • In this paper we study techniques for generating and evaluating confidence bands on ROC curves. (fosterprovost.com)
  • In this study, one cluster was found to have the highest AUC-ROC for each subfield. (hireforstatisticsexam.com)

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