Roc curve. Medical search

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)

In the medical field, the "Area Under Curve" (AUC) is a statistical concept used to evaluate the performance of diagnostic tests or biomarkers. It is a measure of the overall accuracy of a test, taking into account both the sensitivity (the ability of the test to correctly identify those with the disease) and the specificity (the ability of the test to correctly identify those without the disease). The AUC is calculated by plotting the sensitivity and 1-specificity of the test on a graph, with sensitivity on the y-axis and 1-specificity on the x-axis. The AUC is then calculated as the area under this curve, with a value of 1 indicating a perfect test and a value of 0.5 indicating a test that is no better than random guessing. The AUC is commonly used in medical research to compare the performance of different diagnostic tests or biomarkers, and is often reported in publications and presentations. It is also used in clinical practice to help healthcare providers make informed decisions about patient care.

Biological markers, also known as biomarkers, are measurable indicators of biological processes, pathogenic processes, or responses to therapeutic interventions. In the medical field, biological markers are used to diagnose, monitor, and predict the progression of diseases, as well as to evaluate the effectiveness of treatments. Biological markers can be found in various biological samples, such as blood, urine, tissue, or body fluids. They can be proteins, genes, enzymes, hormones, metabolites, or other molecules that are associated with a specific disease or condition. For example, in cancer, biological markers such as tumor markers can be used to detect the presence of cancer cells or to monitor the response to treatment. In cardiovascular disease, biological markers such as cholesterol levels or blood pressure can be used to assess the risk of heart attack or stroke. Overall, biological markers play a crucial role in medical research and clinical practice, as they provide valuable information about the underlying biology of diseases and help to guide diagnosis, treatment, and monitoring.

In the medical field, algorithms are a set of step-by-step instructions used to diagnose or treat a medical condition. These algorithms are designed to provide healthcare professionals with a standardized approach to patient care, ensuring that patients receive consistent and evidence-based treatment. Medical algorithms can be used for a variety of purposes, including diagnosing diseases, determining the appropriate course of treatment, and predicting patient outcomes. They are often based on clinical guidelines and best practices, and are continually updated as new research and evidence becomes available. Examples of medical algorithms include diagnostic algorithms for conditions such as pneumonia, heart attack, and cancer, as well as treatment algorithms for conditions such as diabetes, hypertension, and asthma. These algorithms can help healthcare professionals make more informed decisions about patient care, improve patient outcomes, and reduce the risk of medical errors.

In the medical field, data interpretation and statistical analysis are essential tools used to analyze and understand complex medical data. Data interpretation involves the process of analyzing and making sense of raw data, while statistical analysis involves the use of mathematical and statistical methods to analyze and draw conclusions from the data. Data interpretation and statistical analysis are used in a variety of medical fields, including epidemiology, clinical trials, and public health. For example, in epidemiology, data interpretation and statistical analysis are used to identify patterns and trends in disease incidence and prevalence, as well as to evaluate the effectiveness of interventions aimed at preventing or treating diseases. In clinical trials, data interpretation and statistical analysis are used to evaluate the safety and efficacy of new treatments or medications. This involves analyzing data from clinical trials to determine whether the treatment or medication is effective and safe for use in patients. Overall, data interpretation and statistical analysis are critical tools in the medical field, helping researchers and healthcare professionals to make informed decisions based on data-driven evidence.

Biometry is the scientific study of the measurement and analysis of biological data, particularly in the context of medical research and clinical practice. It involves the use of statistical and mathematical techniques to analyze and interpret data related to the structure, function, and development of living organisms, including humans. In the medical field, biometry is used to measure various biological parameters, such as body size, shape, and composition, as well as physiological and biochemical markers of health and disease. Biometric data can be collected using a variety of techniques, including imaging, laboratory tests, and physical measurements. Biometry is an important tool in medical research, as it allows researchers to quantify and compare biological variables across different populations and study designs. It is also used in clinical practice to diagnose and monitor diseases, as well as to evaluate the effectiveness of treatments and interventions.

Case-control studies are a type of observational study used in the medical field to investigate the relationship between an exposure and an outcome. In a case-control study, researchers identify individuals who have experienced a particular outcome (cases) and compare their exposure history to a group of individuals who have not experienced the outcome (controls). The main goal of a case-control study is to determine whether the exposure was a risk factor for the outcome. To do this, researchers collect information about the exposure history of both the cases and the controls and compare the two groups to see if there is a statistically significant difference in the prevalence of the exposure between the two groups. Case-control studies are often used when the outcome of interest is rare, and it is difficult or unethical to conduct a prospective cohort study. However, because case-control studies rely on retrospective data collection, they are subject to recall bias, where participants may not accurately remember their exposure history. Additionally, because case-control studies only provide information about the association between an exposure and an outcome, they cannot establish causality.

In the medical field, computer simulation refers to the use of computer models and algorithms to simulate the behavior of biological systems, medical devices, or clinical procedures. These simulations can be used to study and predict the effects of various medical interventions, such as drug treatments or surgical procedures, on the human body. Computer simulations in medicine can be used for a variety of purposes, including: 1. Training and education: Medical students and professionals can use computer simulations to practice and refine their skills in a safe and controlled environment. 2. Research and development: Researchers can use computer simulations to study the underlying mechanisms of diseases and develop new treatments. 3. Clinical decision-making: Physicians can use computer simulations to predict the outcomes of different treatment options and make more informed decisions about patient care. 4. Device design and testing: Engineers can use computer simulations to design and test medical devices, such as prosthetics or surgical instruments, before they are used in patients. Overall, computer simulations are a powerful tool in the medical field that can help improve patient outcomes, reduce costs, and advance medical knowledge.

Artificial Intelligence (AI) in the medical field refers to the application of computer algorithms and machine learning techniques to analyze and interpret medical data, with the goal of improving patient outcomes and advancing medical research. AI can be used in a variety of ways in healthcare, including: 1. Medical imaging: AI algorithms can analyze medical images such as X-rays, CT scans, and MRIs to detect abnormalities and assist in diagnosis. 2. Personalized medicine: AI can analyze a patient's genetic data and medical history to develop personalized treatment plans. 3. Drug discovery: AI can analyze large datasets to identify potential new drugs and predict their effectiveness. 4. Electronic health records (EHRs): AI can analyze EHR data to identify patterns and trends that can inform clinical decision-making. 5. Virtual assistants: AI-powered virtual assistants can help patients manage their health by answering questions, providing reminders, and connecting them with healthcare providers. Overall, AI has the potential to revolutionize healthcare by improving diagnosis, treatment, and patient outcomes, while also reducing costs and increasing efficiency.

Liver cirrhosis is a chronic liver disease characterized by the replacement of healthy liver tissue with scar tissue, leading to a loss of liver function. This scarring, or fibrosis, is caused by a variety of factors, including chronic alcohol abuse, viral hepatitis, non-alcoholic fatty liver disease, and autoimmune liver diseases. As the liver becomes increasingly damaged, it becomes less able to perform its many functions, such as filtering toxins from the blood, producing bile to aid in digestion, and regulating blood sugar levels. This can lead to a range of symptoms, including fatigue, weakness, abdominal pain, jaundice, and confusion. In advanced cases, liver cirrhosis can lead to liver failure, which can be life-threatening. Treatment options for liver cirrhosis depend on the underlying cause and may include lifestyle changes, medications, and in some cases, liver transplantation.

Decision Support Techniques (DSTs) are tools and methods used to assist healthcare professionals in making informed decisions. These techniques are designed to provide relevant and accurate information to healthcare providers to help them make better decisions about patient care. In the medical field, DSTs can be used in a variety of ways, including: 1. Diagnosis: DSTs can help healthcare providers diagnose diseases and conditions by analyzing patient data and providing possible diagnoses based on that data. 2. Treatment planning: DSTs can help healthcare providers develop treatment plans for patients by analyzing patient data and providing recommendations for the most effective treatment options. 3. Risk assessment: DSTs can help healthcare providers assess the risk of various medical conditions and develop strategies to reduce that risk. 4. Clinical decision-making: DSTs can help healthcare providers make clinical decisions by providing information on the latest medical research and best practices. 5. Resource allocation: DSTs can help healthcare providers allocate resources more effectively by analyzing patient data and identifying areas where resources are needed most. Overall, DSTs can help healthcare providers make more informed decisions, improve patient outcomes, and reduce the risk of medical errors.

A solitary pulmonary nodule (SPN) is a small, round or oval growth in the lung that appears as a single, well-defined abnormality on a chest X-ray or CT scan. SPNs can be either benign (non-cancerous) or malignant (cancerous), and their size, shape, and location can help determine their likelihood of being cancerous. SPNs are typically less than 3 centimeters in diameter, although some may be larger. They can occur in any part of the lung, but are more common in the upper lobes. SPNs can be classified as solid, part-solid, or ground-glass opacity based on their appearance on imaging studies. The diagnosis of SPNs is often made through a combination of imaging studies, such as chest X-rays and CT scans, and the results of a biopsy, which involves taking a small sample of tissue from the nodule for examination under a microscope. Treatment for SPNs depends on their size, location, and whether they are benign or malignant. Small, non-cancerous SPNs may be monitored with regular imaging studies, while larger or cancerous SPNs may require surgery, radiation therapy, or chemotherapy.

Cohort studies are a type of observational study in the medical field that involves following a group of individuals (a cohort) over time to identify the incidence of a particular disease or health outcome. The individuals in the cohort are typically selected based on a common characteristic, such as age, gender, or exposure to a particular risk factor. During the study, researchers collect data on the health and lifestyle of the cohort members, and then compare the incidence of the disease or health outcome between different subgroups within the cohort. This can help researchers identify risk factors or protective factors associated with the disease or outcome. Cohort studies are useful for studying the long-term effects of exposure to a particular risk factor, such as smoking or air pollution, on the development of a disease. They can also be used to evaluate the effectiveness of interventions or treatments for a particular disease. One of the main advantages of cohort studies is that they can provide strong evidence of causality, as the exposure and outcome are measured over a long period of time and in the same group of individuals. However, they can be expensive and time-consuming to conduct, and may be subject to biases if the cohort is not representative of the general population.

Cross-sectional studies are a type of observational research design used in the medical field to examine the prevalence or distribution of a particular health outcome or risk factor in a population at a specific point in time. In a cross-sectional study, data is collected from a sample of individuals who are all measured at the same time, rather than following them over time. Cross-sectional studies are useful for identifying associations between health outcomes and risk factors, but they cannot establish causality. For example, a cross-sectional study may find that people who smoke are more likely to have lung cancer than non-smokers, but it cannot determine whether smoking causes lung cancer or if people with lung cancer are more likely to smoke. Cross-sectional studies are often used in public health research to estimate the prevalence of diseases or conditions in a population, to identify risk factors for certain health outcomes, and to compare the health status of different groups of people. They can also be used to evaluate the effectiveness of interventions or to identify potential risk factors for disease outbreaks.

In the medical field, calibration refers to the process of verifying and adjusting the accuracy and precision of medical equipment or instruments. Calibration is important to ensure that medical equipment is functioning properly and providing accurate results, which is critical for making informed medical decisions and providing appropriate patient care. Calibration typically involves comparing the performance of the medical equipment to known standards or references. This can be done using specialized equipment or by sending the equipment to a calibration laboratory for testing. The calibration process may involve adjusting the equipment's settings or replacing worn or damaged components to restore its accuracy and precision. Calibration is typically performed on a regular basis, depending on the type of equipment and the frequency of use. For example, some medical equipment may need to be calibrated daily, while others may only require calibration every six months or so. Failure to properly calibrate medical equipment can lead to inaccurate results, which can have serious consequences for patient safety and outcomes.

Optic nerve diseases refer to a group of medical conditions that affect the optic nerve, which is the nerve responsible for transmitting visual information from the retina to the brain. These diseases can cause a range of symptoms, including vision loss, eye pain, and changes in visual perception. Some common optic nerve diseases include: 1. Glaucoma: A group of eye diseases that damage the optic nerve, often caused by elevated pressure inside the eye. 2. Optic neuritis: Inflammation of the optic nerve that can cause vision loss, eye pain, and sensitivity to light. 3. Optic atrophy: A condition in which the optic nerve becomes thin and weak, leading to vision loss. 4. Leber's hereditary optic neuropathy: A genetic disorder that causes progressive vision loss, often starting in young adulthood. 5. Optic nerve drusen: Small deposits of calcium and other minerals that can form on the optic nerve, causing vision loss. 6. Optic nerve glioma: A type of brain tumor that can affect the optic nerve, causing vision loss and other symptoms. Treatment for optic nerve diseases depends on the specific condition and its severity. In some cases, medications or surgery may be used to manage symptoms or slow the progression of the disease. Early detection and treatment are important for preserving vision and preventing further damage to the optic nerve.

Natriuretic Peptide, Brain (NPB) is a hormone that is produced by the brain and released into the bloodstream. It is a member of the natriuretic peptide family, which also includes atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP). NPB has several functions in the body, including regulating blood pressure, fluid balance, and heart rate. It works by inhibiting the release of renin, a hormone that stimulates the production of angiotensin II, which in turn constricts blood vessels and increases blood pressure. NPB also has a role in the regulation of the autonomic nervous system, which controls heart rate and blood pressure. It can stimulate the release of nitric oxide, a molecule that helps to relax blood vessels and lower blood pressure. In the medical field, NPB is being studied as a potential diagnostic tool for various cardiovascular diseases, including heart failure and hypertension. It may also have therapeutic potential for these conditions, as it has been shown to improve cardiac function and reduce blood pressure in animal models.

Cervical ripening refers to the process of softening and thinning the cervix in preparation for childbirth. The cervix is the lower part of the uterus that opens up during labor to allow the baby to pass through. During pregnancy, the cervix is typically closed and firm to prevent the baby from coming out too early. However, as labor approaches, the cervix begins to change and soften in response to hormones produced by the body. This process is called cervical ripening. Cervical ripening can be induced by a healthcare provider using medications or other methods. The goal of cervical ripening is to help the cervix open up and dilate more quickly, which can help speed up labor and delivery.

... 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 ...

https://en.wikipedia.org/wiki/Partial_Area_Under_the_ROC_Curve

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 ...

https://en.wikipedia.org/wiki/MedCalc

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 ( ...

https://en.wikipedia.org/wiki/Receiver_Operating_Characteristic_Curve_Explorer_and_Tester

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". ...

https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers

Evaluation = Confusion Matrix, Risk Charts, Cost Curve, Hand, Lift, ROC, Precision, Sensitivity. Charts = Box Plot, Histogram, ...

https://en.wikipedia.org/wiki/Rattle_GUI

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: ...

https://en.wikipedia.org/wiki/Diagnostic_odds_ratio

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 ...

https://en.wikipedia.org/wiki/Biometrics

It achieved an area under the ROC (Receiver Operating Characteristic) curve of 0.89. To provide explain-ability, they developed ...

https://en.wikipedia.org/wiki/Predictive_modelling

... 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 ...

https://en.wikipedia.org/wiki/David_Hand_(statistician)

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 ...

https://en.wikipedia.org/wiki/Total_operating_characteristic

... such as the area under the ROC-curve. Bias is the extent to which one response is more probable than another, averaging across ...

https://en.wikipedia.org/wiki/Detection_theory

Includes a tool for grading and generating ROC curves from resultant sam files. Open-source, written in pure Java; supports all ...

https://en.wikipedia.org/wiki/List_of_RNA-Seq_bioinformatics_tools

"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 ...

https://en.wikipedia.org/wiki/Cross-validation_(statistics)

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. ...

https://en.wikipedia.org/wiki/Mann–Whitney_U_test

Several statistical methods may be used to evaluate the algorithm, such as ROC curves. If the learned patterns do not meet the ...

https://en.wikipedia.org/wiki/Data_mining

The area under the receiver operating characteristic (ROC) curve is widely used to evaluate its performance. Resulting hits ...

https://en.wikipedia.org/wiki/Docking_(molecular)

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 ...

https://en.wikipedia.org/wiki/Gini_coefficient

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". ...

https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research

More exotic fitness functions that explore model granularity include the area under the ROC curve and rank measure. Also ...

https://en.wikipedia.org/wiki/Gene_expression_programming

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 ...

https://en.wikipedia.org/wiki/Receiver_operating_characteristic

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 ...

https://en.wikipedia.org/wiki/Net_reclassification_improvement

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 ...

https://en.wikipedia.org/wiki/Demining

... 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. ...

https://en.wikipedia.org/wiki/Computational_Resource_for_Drug_Discovery

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' ...

https://en.wikipedia.org/wiki/Cumulative_accuracy_profile

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 ...

https://en.wikipedia.org/wiki/Evidence-based_medicine

... is the area under the ROC curve (AUC). Some example results of PGS performance, as measured in AUC (0 ≤ AUC ≤ 1 where a larger ...

https://en.wikipedia.org/wiki/Polygenic_score

When the features represent distinguishable patterns of burst and suppression, a fixed threshold using ROC-curve or machine ...

https://en.wikipedia.org/wiki/Burst_suppression

The image below shows an ROC curve, measuring the probability of detection over the probability of false detection, as well as ...

https://en.wikipedia.org/wiki/One-shot_learning_(computer_vision)

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 ...

https://en.wikipedia.org/wiki/Materials_MASINT

More recently, receiver operating characteristic (ROC) curves have been used to evaluate the tradeoff between true- and false- ...

https://en.wikipedia.org/wiki/Statistical_classification

Based on analysis of the ROC curves, a suitable score cutoff was chosen for the prediction of cleavage sites in lanthipeptides ...

https://en.wikipedia.org/wiki/Machine_learning_in_bioinformatics

To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools ... Results: With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions ... ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are ... and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study ...

https://www.sciweavers.org/publications/proc-open-source-package-r-and-s-analyze-and-compare-roc-curves

I can then plot the ROC curve: on wikipedia I find that the bisector of the ROC spaces is equivalent to a random guess. ... The ROC curve measures how well the model can distinguish between the two categories: the higher the AUC score, the better the ... ROC curve and thresholds: why does it never have the ideal point at the top left for observations close to certainty? ... begingroup$ ROC curves are only appropriate when doing retrospective sampling e.g. case-control designs (to align with the ...

https://stats.stackexchange.com/questions/549153/is-the-roc-curve-sufficient-for-rejecting-the-null-hypotesis-in-binary-classific

"ROC Curve" by people in UAMS Profiles by year, and whether "ROC Curve" was a major or minor topic of these publications. ... "ROC Curve" is a descriptor in the National Library of Medicines controlled vocabulary thesaurus, MeSH (Medical Subject ... Below are the most recent publications written about "ROC Curve" by people in Profiles over the past ten years. ... Below are MeSH descriptors whose meaning is more general than "ROC Curve". ...

https://uams-triprofiles.uams.edu/profiles/display/105379

Receiver operating characteristic (ROC) curve analysis. To verify the accuracy of screened hub genes, we performed ROC curve ... ROC curves were generated with the R package "pROC", and the corresponding AUC values were calculated. Students t test was ... ROC curve validated the diagnostic significance of hub genes for AD in training dataset GSE48350 (A) and external validation ... We further performed ROC curve analysis of the male and female samples in GSE48350 to evaluate the gender-specific effect of ...

https://www.nature.com/articles/s41598-023-27977-7?error=cookies_not_supported

When you do not dispose of raw data to perform ROC curve analysis, you can still calculate positive and negative predictive ... ROC curves. Predictive values. Description. When you do have access to the raw data to perform ROC curve analysis, you can ... ROC curves: Introduction *Interactive dot diagram *Plot versus criterion values *Interval likelihood ratios *Comparison of ROC ... ROC curve analysis: predictive values. Command:. Statistics. ... www.medcalc.org/manual/roc-curve-analysis-predictive-values.php ...

https://www.medcalc.org/manual/roc-curve-analysis-predictive-values.php

To understand the ROC curve well, it is good to understand the three characteristics shown in the ROC curve plot:. *True ... In summary, the ROC curve is a curve that represents the performance of a binary classifier, and it shows the ratio of FPR and ... In short, the ROC curve represents a better classifier when the curve is closer to the upper left corner. ... Ultimately, a ROC curve that is closer to the upper-left corner indicates a better binary classifier. As a professional math ...

https://angeloyeo.github.io/2020/08/05/ROC_en.html

Solutions for data science: find workflows, nodes and components, and collaborate in spaces.

https://hub.knime.com/search?type=All&tag=prediction,ROC%20Curve,partitioning&sort=maxKudos

The ROC (Receiver Operating Characteristic) curve analysis was used to assess the level of diagnostic accuracy through indexes ... The ROC curve analysis. Rev. psicol. (Lima) [online]. 2017, vol.35, n.1, pp. 167-192. ISSN 0254-9247. http://dx.doi.org/ ... of the Area below the curve (ABC), sensitivity (S) and specificity (E). The analysis was differentiated by gender and showed ...

http://pepsic.bvsalud.org/scielo.php?script=sci_abstract&pid=S0254-92472017000100006&lng=es&nrm=iso&tlng=en

ROC curve analysis. A ROC comparison of IL-18, S100A8 and S100A9 was conducted to determine the predictive values of these ... Figure 2 ROC curves of S100A9, IL-18, S100A8, and IL-6 for predicting systemic-onset juvenile idiopathic arthritis patients. ... ROC curves of S100A9, IL-18, S100A8, and IL-6 for predicting systemic-onset juvenile idiopathic arthritis patients. ... ROC curves of S100A9, IL-18, S100A8, and IL-6 for predicting systemic-onset juvenile idiopathic arthritis patients. ...

https://www.scielo.br/j/bjmbr/a/YjW9hrdHZbdJShk79Bfpy8C/?lang=en

PRC Curve與 ROC Curve在功能上極度相似，也是常見的評估指標， ... ROC-AUC），面積越大代表不論閾值設定為何，Sensitivity 與 Specificity 的平均效果會越好，模型效果也就會越好。 3. 定義PRC Curve是由什麼組成 PRC Curve 參考了 ROC Curve 的功能，近五年蓬勃發展的 ... 定義 ROC Curve 是由什麼組成 ROC Curve 是由

https://www.cupoy.com/qa/club/ai_tw/0000016D6BA22D97000000016375706F795F72656C656173654B5741535354434C5542/0000017D9A1719BB000000046375706F795F72656C656173655155455354

ROC Curve PRO. The ROC Curve analysis can be used to test a diagnostic to determine if an incident had occurred, or compare the ... For example, you can use ROC Curve analysis to test a diagnostic to determine if an incident had occurred, or compare the ... ROC (Receiver Operating Characteristic) curve analysis is mainly used for diagnostic studies in Clinical Chemistry, ... ROC) curves, power and sample size calculations, and nonparametric tests are available in OriginPro. ...

https://www.originlab.com/index.aspx?go=Products/Origin/Statistics#ANOVA

Smooth Time-Dependent ROC Curve Estimation. Computes smooth estimations for the Cumulative/Dynamic and Incident/Dynamic ROC ... curves, in presence of right censorship, based on the bivariate kernel density estimation of the joint distribution function of ...

https://stat.ethz.ch/CRAN/web/packages/smoothROCtime/index.html

Empirical fragility and ROC curves for masonry buildings subjected to settlements. Alfonso Prosperi / Paul A. Korswagen / Mandy ...

https://www.tudelft.nl/staff/a.prosperi/

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 ...

https://www.cdc.gov/ncbddd/hearingloss/guidancemanual/chapter4.html

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 ...

https://stripe.com/en-gb-jp/guides/primer-on-machine-learning-for-fraud-protection

ROC) curve analysis showed an area under the curve for prediction of nonviable myocardium, as determined by (18)FDG PET using ... whereas the area under the ROC curve using tissue Doppler imaging was 0.63 (95% CI 0.61 to 0.65). ...

https://pubmed.ncbi.nlm.nih.gov/11823082/?dopt=Abstract

ROC curves. (a) Receiver-operating characteristic (ROC) curves for SYNTAX score II and anatomical SYNTAX in predicting 1-year ... ROC curves showed an improved area under the curve (AUC) when comparing SxSII with SxS regarding all-cause mortality at 1 year ... AUC = area under the curve; CI = confidence intervals. (b) Receiver-operating characteristic (ROC) curves for SYNTAX score II, ... ROC curves were constructed to assess the ability of the SxSII, SxS, and GRACE risk score to predict events at 1-year follow-up ...

https://www.hindawi.com/journals/crp/2018/9762176/

ROC) curves and calculate the areas under the curve (AUCs). We also performed these ROC analyses on the allele count model for ... ROC curve.. We evaluated the discriminatory power of a genetic test based on the 18 type 2 diabetes variants by calculating the ... Weighting variants and the optimal ROC curve.. The simple allele count model we used for some of our analyses of "extremes" ... Using the general model (as opposed to the additive model, which assumes equal and additive effects), the ROC curve for the 18 ...

https://diabetesjournals.org/diabetes/article/57/11/3129/13485/Assessing-the-Combined-Impact-of-18-Common-Genetic

Receiver Operating Characteristic (ROC) Curves. We used ROC curves to describe the ability of each scoring scheme to ... The ROC curves based on this more stringent test were very similar to the original curves (Supplementary Fig. S4), ... Supplementary Figure S4 ROC and ROC-like curves for high-information-content positions in transcription factor binding sites.. ... curve for elements active in HUVEC (as in Supplementary Fig. S2A); and (C) a ROC curve based on elements active in various cell ...

https://www.biorxiv.org/content/10.1101/006825v3.full

Erratum to "ROC curves for clinical prediction models part 1. ROC plots showed no added value above the AUC when evaluating the ... Erratum to "ROC curves for clinical prediction models part 1. ROC plots showed no added value above the AUC when evaluating the ...

https://www.ndorms.ox.ac.uk/publications/1148750

Plotting an ROC curve in glmnet Aug 4, 2014. ...

https://stackoverflow.com/users/2225428/niubibang

We have provided the AUC-ROC curve in Figure 4, Figure 5 and Figure 6 for three prediction models which performed the best in ... is the order of points in the original curves and S. n. P. =. d. 1. ,. d. 2. ,. d. 3. ,. …. ,. d. P. is the order of points ... We have adopted 10-fold cross-validation for evaluating specificity, sensitivity, AUC-ROC curve performance. ... 0.971 sensitivity and 0.954 area under the receiver operating characteristic curve (AUC-ROC). In another study [19], an ...

https://www.mdpi.com/1422-0067/23/7/3701

Model accuracy reports (ROC curve). When governance approves the metadata, the model gets published to a binary repository. The ...

https://www.thoughtworks.com/en-sg/insights/blog/using-cd-machine-learning-models-tackle-fraud

A scoring system was developed using these predictors with an area under the receiver operating characteristic curve of 0.71 ( ... showing an ROC curve with an AUC of 0.71 (95% CI: 0.64-0.78). AUC = area under the receiver operating characteristic curve; ... showing an ROC curve with an AUC of 0.71 (95% CI: 0.64-0.78). AUC = area under the receiver operating characteristic curve; ... showing an ROC curve with an AUC of 0.71 (95% CI: 0.64-0.78). AUC = area under the receiver operating characteristic curve; ...

https://www.ajtmh.org/view/journals/tpmd/108/6/article-p1244.xml?rskey=NJXqBK&result=3

The diagnostic test represented by the unbroken ROC curve is a better test than that represented by the broken ROC curve, as ... Receiver-operating characteristic curves (ROC). Each point along a ROC represents the trade-off in sensitivity and specificity ... demonstrated by its greater sensitivity for any given specificity (and thus, greater area under the curve). ...

https://wwwnc.cdc.gov/eid/article/10/1/03-0243-f2

Area Under ROC Curve (95% CI). IgG to Rta +. IgA to EBNA1 ...

https://emedicine.medscape.com/article/848163-overview

If I plotted the ROC curve, theyd be smack in the middle. ...

https://www.techdirt.com/2010/09/03/hurt-locker-subpoenas-arrive-with-new-language-and-higher-demands/

As per my little understanding, the term ROC stands for Receiver Operating Characteristic. ROC curves were first employed in ... ROC curves are used in clinical biochemistry to choose the most appropriate cut-off for a test. The best cut-off has the ... As the area under an ROC curve is a measure of the usefulness of a test in general, where a greater area means a more useful ... ROC curves are frequently used to show in a graphical way the connection/trade-off between clinical sensitivity and specificity ...

https://www.researchgate.net/topic/Transfusion

A novel biometric approach to generate ROC curve from the Probabilistic Neural Network. In: 24th Signal Processing and ...

https://www.ncl.ac.uk/engineering/staff/profile/satnamdlay.html

Background: Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. (sciweavers.org)
The ROC (Receiver Operating Characteristic) curve displays the performance of a binary classifier for various thresholds. (github.io)
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)
Advanced statistical analysis tools, such as repeated measures ANOVA, multivariate analysis, receiver operating characteristic (ROC) curves, power and sample size calculations, and nonparametric tests are available in OriginPro. (originlab.com)
Receiver operating characteristic (ROC) curve analysis showed an area under the curve for prediction of nonviable myocardium, as determined by (18)FDG PET using SRI, of 0.89 (95% confidence interval [CI] 0.88 to 0.90), whereas the area under the ROC curve using tissue Doppler imaging was 0.63 (95% CI 0.61 to 0.65). (nih.gov)
The discriminatory ability of the combined SNP information was assessed by grouping individuals based on number of risk alleles carried and determining relative odds of type 2 diabetes and by calculating the area under the receiver-operator characteristic curve (AUC). (diabetesjournals.org)
Receiver-operating characteristic curves (ROC). (cdc.gov)

Erratum to "ROC curves for clinical prediction models part 1. (ox.ac.uk)
ROC plots showed no added value above the AUC when evaluating the performance of clinical prediction models" [J Clin Epidemiol. (ox.ac.uk)

When you do have access to the raw data to perform ROC curve analysis, you can still calculate positive and negative predictive values for a test when the sensitivity and specificity of the test as well as the disease prevalence (or the pretest probability of disease) are known, using Bayes' theorem. (medcalc.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)

Preliminary laboratory evaluations using the MedCalc™ ROC curve analysis software has been performed. (cdc.gov)

I'm feeling I can't because I don't have 'error bars' on the ROC curve: if I train several classifier with the same parameters but different train/test splitting would it be sufficient? (stackexchange.com)
In short, the ROC curve represents a better classifier when the curve is closer to the upper left corner. (github.io)
In summary, the ROC curve is a curve that represents the performance of a binary classifier, and it shows the ratio of FPR and TPR for all possible thresholds. (github.io)

To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, objectoriented and flexible interface. (sciweavers.org)
A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. (sciweavers.org)

0.001) was observed compared to clinical bedside evaluation, with an area under the ROC curve of 0,765. (bvsalud.org)

Results: With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. (sciweavers.org)
Statistical modeling, based on receiving operating characteristic curves, suggests that three to five isolates may be necessary to accurately assign nasal carriage status for these more variable characteristics. (cdc.gov)

image](http://kwassistfile.cupoy.com/0000017D9A1927A3000000036375706F795F72656C65617365414E53/1637748332344/large) 而曲線下方包含的面積就是 The Area Under the ROC Curve （簡稱為：ROC-AUC），面積 (cupoy.com)
image](http://kwassistfile.cupoy.com/0000017D9A1927A3000000036375706F795F72656C65617365414E53/1637748332345/large) 而曲線下方包含的面積就是 The Area Under the PRC Curve （簡稱為：PRC-AUC），面積 (cupoy.com)

The ROC curve measures how well the model can distinguish between the two categories: the higher the AUC score, the better the ability to distinguish (at least a bit loosely speaking). (stackexchange.com)
As shown in the following figure, if we can distinguish the two classes better, the ROC curve moves closer to the upper-left corner. (github.io)

This graph shows the total number of publications written about "ROC Curve" by people in UAMS Profiles by year, and whether "ROC Curve" was a major or minor topic of these publications. (uams.edu)

Below are the most recent publications written about "ROC Curve" by people in Profiles over the past ten years. (uams.edu)

Is then the null-hypothesis rejection valid only for the probability thresholds where the ROC curve (together with the error bar described in the previous point) is above the ROC space bisector? (stackexchange.com)
What does a point on the ROC curve mean? (github.io)
Changes in the position of the point on the ROC curve as the threshold varies. (github.io)

Is the ROC curve sufficient for rejecting the null hypotesis in binary classifications? (stackexchange.com)

The False Positive Rate (FPR) and True Positive Rate (TPR) represent the values displayed on the x and y axes of an ROC curve, respectively. (github.io)

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)

begingroup$ ROC curves are only appropriate when doing retrospective sampling e.g. case-control designs (to align with the conditioning used for the points on the ROC which condition on the future to predict the past) and you also seem to be wanting to use forced-choice classification when probability estimation should be the goal. (stackexchange.com)

We have generated fitCons scores for three human cell types based on public data from EN-CODE. (biorxiv.org)

COVID-19 screening scores of asymptomatic patients undergoing a medical procedure, showing an ROC curve with an AUC of 0.71 (95% CI: 0.64-0.78). (ajtmh.org)

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)
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)
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)
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)
AUC-ROC is the acronym for Area Under the Receiver Operating Characteristic Curve. (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)
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)
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)
3. A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method. (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)
16. Advantages to transforming the receiver operating characteristic (ROC) curve into likelihood ratio co-ordinates. (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)

6. Transformation-invariant and nonparametric monotone smooth estimation of ROC curves. (nih.gov)
9. Bayesian bootstrap estimation of ROC curve. (nih.gov)
11. Nonparametric estimation of ROC curves in the absence of a gold standard. (nih.gov)
17. Semi-parametric estimation of the binormal ROC curve for a continuous diagnostic test. (nih.gov)
Generalized Estimation Equations analysed workplace and task effects on the activity level and counts-per-minute, and kappa statistics and ROC curves analysed the cut-point validity. (biomedcentral.com)

7. Equivalence of binormal likelihood-ratio and bi-chi-squared ROC curve models. (nih.gov)
14. Tests of equivalence and non-inferiority for diagnostic accuracy based on the paired areas under ROC curves. (nih.gov)

5. A comparison of parametric and nonparametric approaches to ROC analysis of quantitative diagnostic tests. (nih.gov)

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)
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)

ROC curve regression analysis: the use of ordinal regression models for diagnostic test assessment. (nih.gov)

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)

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)

18. A non-inferiority test for diagnostic accuracy based on the paired partial areas under ROC curves. (nih.gov)

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)

Shows or hides the lift curve for the model. (jmp.com)

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)

12. Recent advances in observer performance methodology: jackknife free-response ROC (JAFROC). (nih.gov)

If you used validation, Lift curve is shown for each of the Training, Validation, and Test sets, if specified. (jmp.com)

Compare up to 10 independent or correlated ROC curves. (analyse-it.com)

This curve is plotted with TPR (True Positive Rate) on the y-axis and FPR (False Positive Rate) on the x-axis . (onlineinterviewquestions.com)

Diseases3

Chemicals and Drugs3

Analytical, Diagnostic and Therapeutic Techniques and Equipment46

Phenomena and Processes8

Information Science12

Health Care25

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

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

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

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

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

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

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

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

ROC Curve

Area Under Curve

Sensitivity and Specificity

Predictive Value of Tests

Reproducibility of Results

Biological Markers

Algorithms

Models, Statistical

Prospective Studies

Observer Variation

Diagnosis, Computer-Assisted

Retrospective Studies

Prognosis

Neural Networks (Computer)

Logistic Models

False Positive Reactions

Radiography

Data Interpretation, Statistical

Severity of Illness Index

Risk Assessment

Biometry

Image Interpretation, Computer-Assisted

Likelihood Functions

Diagnostic Techniques, Endocrine

Diagnostic Techniques and Procedures

Epidemiologic Methods

Diagnostic Tests, Routine

Tumor Markers, Biological

Radiographic Image Enhancement

Risk Factors

Case-Control Studies

Discriminant Analysis

Computer Simulation

Time Factors

Diagnostic Techniques, Ophthalmological

Artificial Intelligence

Labor Onset

Reference Values

Liver Cirrhosis

Decision Support Techniques

Solitary Pulmonary Nodule

Radiographic Image Interpretation, Computer-Assisted

Immunoassay

Cohort Studies

Cross-Sectional Studies

Image Enhancement

Multivariate Analysis

Treatment Outcome

Enzyme-Linked Immunosorbent Assay

Calibration

Optic Nerve Diseases

Tomography, X-Ray Computed

Nephelometry and Turbidimetry

Reference Standards

Support Vector Machines

Natriuretic Peptide, Brain

Cervical Ripening

Probability

Mass Screening

Early Diagnosis

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

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

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

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

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

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

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

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

Partial Area Under the ROC Curve

MedCalc

Receiver Operating Characteristic Curve Explorer and Tester

Evaluation of binary classifiers

Rattle GUI

Diagnostic odds ratio

Biometrics

Predictive modelling

David Hand (statistician)

Total operating characteristic

Detection theory

List of RNA-Seq bioinformatics tools