In clinical practice, dentists are faced with the dilemma of whether to treat, maintain, or extract a tooth. Of primary importance are the patient s desires and the restorability and periodontal condition of the tooth/teeth in question. Too often, clinicians extract teeth when endodontic therapy, crown-lengthening surgery, forced orthodontic eruption, or regenerative therapy can be used with predictable results. In addition, many clinicians do not consider the use of questionable teeth as provisional or transitional abutments. The aim of this article is to present a novel decision tree approach that will address the clinical deductive reasoning, based on the scientific literature and exemplified by selective case presentations, that may help clinicians make the right decision. Innovative decision tree algorithms will be proposed that consider endodontic, restorative, and periodontal assessments to improve and possibly eliminate erroneous decision making. Decision-based algorithms are dynamic and ...
Downloadable! This paper describes an improved method for investment decision making. The method, which is called the stochastic decision tree method, is particularly applicable to investments characterized by high uncertainty and requiring a sequence of related decisions to be made over a period of time. The stochastic decision tree method builds on concepts used in the risk analysis method and the decision tree method of analyzing investments. It permits the use of subjective probability estimates or empirical frequency distributions for some or all factors affecting the decision. This application makes it practicable to evaluate all or nearly all feasible combinations of decisions in the decision tree, taking account of both expected value of return and aversion to risk, thus arriving at an optimal or near optimal set of decisions. Sensitivity analysis of the model can highlight factors that are critical because of high leverage on the measure of performance, or high uncertainty, or both. The method
Predicting blood-brain barrier (BBB) permeability is essential to drug development, as a molecule cannot exhibit pharmacological activity within the brain parenchyma without first transiting this barrier. Understanding the process of permeation, however, is complicated by a combination of both limited passive diffusion and active transport. Our aim here was to establish predictive models for BBB drug permeation that include both active and passive transport. A database of 153 compounds was compiled using in vivo surface permeability product (logPS) values in rats as a quantitative parameter for BBB permeability. The open source Chemical Development Kit (CDK) was used to calculate physico-chemical properties and descriptors. Predictive computational models were implemented by machine learning paradigms (decision tree induction) on both descriptor sets. Models with a corrected classification rate (CCR) of 90% were established. Mechanistic insight into BBB transport was provided by an Ant Colony
Background Hospital infections with multiresistant bacteria, e.g., Methicillin-resistant Staphylococcus aureus (MRSA), cause heavy financial burden worldwide. Rapid and precise identification of MRSA carriage in combination with targeted hygienic management are proven to be effective but incur relevant extra costs. Therefore, health care providers have to decide which MRSA screening strategy and which diagnostic technology should be applied according to economic criteria. Aim The aim of this study was to determine which MRSA admission screening and infection control management strategy causes the lowest expected cost for a hospital. Focus was set on the Point-of-Care Testing (PoC). Methods A decision tree analytic cost model was developed, primarily based on data from peer-reviewed literature. In addition, univariate sensitivity analyses of the different input parameters were conducted to study the robustness of the results.
I am looking for a good decision tree algorithm written in C# or at least .NET, so far my searches have not turned up much fruit. Could anyone suggest one?
Decision trees are weak models. Theyre very expandable but they dont perform very well. But lets see how we can improve the performance of decision trees. But first of all lets understand those. So, consider this dataset, its a dataset which helps you to decide whether to go for tennis training or not. For example, if the weather outlook is sunny, the temperature is hot and the humidity is high, and it is not windy, you dont play. On the other hand, if the outlook is overcast, it is hot, you have high humidity and its not windy, youll play. So you can also express this table as a so called decision tree. So first of all you check the outlook. If its overcast, you definitely never go. But if its sunny and humid, then you dont go. If its sunny and normal, you go. If its rainy and windy, you go. And if its rainy and not windy, you dont go. For whatever reason, I dont care, but thats basically the decision tree reflecting those data. So actually to build such a tree to check value, ...
Linear regression models are based on the Microsoft Decision Trees algorithm. However, even if you do not use the Microsoft Linear Regression algorithm, any decision tree model can contain a tree or nodes that represent a regression on a continuous attribute.. You do not need to specify that a continuous column represents a regressor. The Microsoft Decision Trees algorithm will partition the dataset into regions with meaningful patterns even if you do not set the REGRESSOR flag on the column. The difference is that when you set the modeling flag, the algorithm will try to find regression equations of the form a*C1 + b*C2 + ... to fit the patterns in the nodes of the tree. The sum of the residuals is calculated, and if the deviation is too great, a split is forced in the tree. For example, if you are predicting customer purchasing behavior using Income as an attribute, and set the REGRESSOR modeling flag on the column, the algorithm would first try to fit the Income values by using a standard ...
Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper ...
OBJECTIVE: The American College of Rheumatology (ACR) 1987 criteria for rheumatoid arthritis (RA) can be applied in 2 formats, a standard x/y list and a decision tree. This study evaluated the performance of the decision tree compared with the list approach in the ascertainment of RA in subjects with new-onset inflammatory polyarthritis (IP) over the first 5 years of observation. Moreover, the use of clinical surrogates to substitute for missing rheumatoid factor (RF) and radiologic erosion data was assessed for validity and for its influence on the resulting RA prevalence estimates. METHODS: In this population-based prospective study, 848 subjects with new-onset IP were interviewed and examined at baseline, with followup at 1, 2, 3, and 5 years. RF and erosion status were determined at prespecified time points. The list criteria were applied cumulatively, while the decision tree was applied cross-sectionally using either data surrogates or the actual reported data. RA prevalence in the 848 subjects
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A variant of unknown significance (VUS) is a variant form of a gene that has been identified through genetic testing, but whose significance to the organism function is not known. An actual challenge in precision medicine is to precisely identify which detected mutations from a sequencing process have a suitable role in the treatment or diagnosis of a disease. The average accuracy of pathogenicity predictors is 85%. However, there is a significant discordance about the identification of mutational impact and pathogenicity among them. Therefore, manual verification is necessary for confirming the real effect of a mutation in its casuistic. In this work, we use variables categorization and selection for building a decision tree model, and later we measure and compare its accuracy with four known mutation predictors and seventeen supervised machine-learning (ML) algorithms. The results showed that the proposed tree reached the highest precision among all tested variables: 91% for True Neutrals, 8% for
In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based scan mask. We analyze the advantages of a sequential raster scan for the block-based scan mask, and integrate the block-connected relationships using two different procedures with binary decision trees to reduce unnecessary memory access. This greatly simplifies the pixel locations of the block-based scan mask. Furthermore, our algorithm significantly reduces the number of leaf nodes and depth levels required in the binary decision tree. We analyze the labeling performance of the proposed algorithm alongside that of other labeling algorithms using high-resolution images and foreground images. The experimental
Decision tree downloads : Insight Tree, GATree.exe, DTREG, Binary Browser, Real Options Valuation, Datashake desktop, SymbioTest Probe, LANDPARK MANAGER, Hovitaga OpenSQL Editor, gvSIG, SmartGit/Hg, Auslogics Browser Care
A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.
A method and system are disclosed for generating a decision-tree classifier from a training set of records, independent of the system memory size. The method comprises the steps of: generating an attribute list for each attribute of the records, sorting the attribute lists for numeric attributes, and generating a decision tree by repeatedly partitioning the records using the attribute lists. For each node, split points are evaluated to determine the best split test for partitioning the records at the node. Preferably, a gini index and class histograms are used in determining the best splits. The gini index indicates how well a split point separates the records while the class histograms reflect the class distribution of the records at the node. Also, a hash table is built as the attribute list of the split attribute is divided among the child nodes, which is then used for splitting the remaining attribute lists of the node. The created tree is further pruned based on the MDL principle, which encodes the
The Complete Risk and Decision Analysis Toolkit Wouldnt you like to know the chances of making money on your next venture? Or which of many decision options is most likely to yield the best payoff? How about the best sequential drilling strategy? Or how much to invest in various projects in order to maximize the return on your project portfolio? Everyone would like answers to these types of questions. Armed with that kind of information, you could take a lot of guesswork out of big decisions and plan strategies with confidence. With the DecisionTools Suite, you can answer these questions and more - right in your Excel spreadsheet. The DecisionTools Suite is an integrated set of programs for risk analysis and decision making under uncertainty that runs in Microsoft Excel. The DecisionTools Suite includes @RISK, which adds risk analysis to Excel using Monte Carlo simulation, PrecisonTree for visual decision tree analysis, TopRank for what-if analysis, NeuralTools and StatTools for data analysis, and
Data Mining and Knowledge Discovery in Healthcare Organizations: A Decision-Tree Approach: 10.4018/978-1-59904-951-9.ch152: Health care organizations are struggling to find new ways to cut healthcare utilization and costs while improving quality and outcomes. Predictive models that
Although some of these methods may seem futuristic, the concepts are sound and are being implemented throughout the international swine industry. Therefore, it is important that practitioners critically evaluate existing gilt development programs within their clients herds to determine whether any of these strategies can be applied to enhance PRRSV control.. Replacement boars should be handled in a similar manner. At this time, all systems appear to be functioning extremely well; therefore, the choice of the development strategy depends on the size of the herd, the status of existing facilities, future expansion plans, availability of commercial PRRSV vaccines, and any potential economic constraints. Decision tree analysis (Figure 6) can be used to determine the optimal strategy for any given herd.. Based on my experiences over the last 4 years, I speculate that the most advantageous system would consist of purchasing PRRS-negative Isowean(TM) pigs, a program of vaccination in the nursery ...
Healthcare providers can use a decision-tree tool to screen women who have gestational diabetes (GDM) for obstructive sleep apnea (OSA), new research from Thailand reports. The results of the study will be presented in a poster Saturday, April 1, at ENDO 2017, the annual scientific meeting of the Endocrine Society, in Orlando, Fla.
The purpose of this study was to determine the cost of overweight and obesity with regard to CV events. To accomplish this, three different data sources were used to acquire the most appropriate inputs needed to conduct such an analysis at a national level. The decision tree cost analysis showed that patients with higher BMI values have higher costs, in terms of CV events, than patients with lower BMI values from the perspective of employer health plans using observational data. One of the strengths of this study is that it is the first to report costs of BMI groups with regard to CV events when considering specific combinations of RFs. The decision tree model could be used by employer health plans to see the 1-year costs of specific subgroups within the model (e.g., average cost of males with a BMI ≥35 and all 3 RF) to allow more targeted interventions for high-cost groups.. In the current analysis, the number of RF increased as BMI increased and the percentage of CV events increased as the ...
Answer to: Decision tree is a concept based on? a) Bill of material b) Probabilities and payoff of the events and alternative decisions c) Goals...
好,我們剛剛提了在 decision tree 的時候我們怎麼利用 post programming 這些方法來使得避免你產生 decision tree 過就 overfit 你的 data。 那我們現在來談一個更艱難一點的,在 machine learning 裡面更艱難的問題是 如果我有不同的 learning model,這裡面甚至不一定只是在 decision tree,譬如我有 You will never 我有 decision tree,那我怎麼比較這個 [聽不清] 為什麼比另外一個,這個 learning model 或 [聽不清] 比另外一個來得好或者不好。 好,那我們,specifically 我們要討論下面的這幾件事情,就是我們需要討論 你的 performance 的結果到底好或不好,所以我們需要一個 metric 我們需要一個 metric,那怎麼 evaluate 這件事情,或指標怎麼量化這個東西。 那接下來,有指標還不夠,你還需要有個方法能夠 穩定地拿到這個指標,因為這個很多是一些,裡面有一些隨機程序,譬如說 randam ...
This course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees. In addition, this course examines many of the auxiliary uses of trees such as exploratory data analysis, dimension reduction, and missing value imputation.
Matlab and Mathematica Projects for ₹600 - ₹1500. 1.Implementing a single-node decision tree: Write functions which take a data set and compute the optimal decision plane. The input set of instances can be of two or more dimensions. The output from t...
Enroll in this tutorial to learn about predictive modeling techniques, including logistic regression, decision trees and neural networks.
An object is to provide an internal abnormality diagnosis method, an internal abnormality diagnosis system and a decision tree generation method for internal abnormality diagnosis of an oil-filled ele
Hi, the minutes and link to meeting recording for the May 19th meeting of the content sub-team of the PDDI Info Model Task Force are pasted below. Kind regards, -R Minutes for 5/19/2016 (Content subgroup) In Attendance: Evan Draper, Brian LeBaron, Richard Boyce, Dan Malone, John Poikonen, Michel Dumontier, Scott Nelson, Jeff Nielsen, Serkan Ayvaz, John Horn, Elizabeth Garcia, Louisa Zhang Meeting recording:http://goo.gl/lESwy5 Meeting: * Introductions * Refresher from last meeting o Agreeing on interactions to work on o Decision trees for certain interactions * Decision Trees o Beta blocker+Epinephrine, Warfarin-NSAIDs; K-sparing diuretics/KCl just added o Goal is to create decision trees for selected interactions that identify clinical consequences, patient factors, specific drugs involved, specific actions to take o Qualtrics survey sent to Standards Team; results pending + User-centered definition of clinical consequence o Beta blocker+Epinephrine and Warfarin+NSAIDs decision trees * ...
Journal CME April - A methodological comparison of risk scores versus decision trees for predicting drug-resistant infections: A case study using extended-spectrum beta-lactamase (ESBL) ...
View Notes - HW8_sol from CSE 21 at UCSD. CSE21 FA11 Homework #8 (11/17/11) 8.1 Solution. Draw the decision tree. Then compute: (a) P r(M2 = red) = r+d r b r + . r+b+cr+b r+b+dr+b (b) P r(M2 = red|M1
Methods Data from RA patients enrolled in the TEMPO trial were analysed. Classification and regression trees were used to develop and validate decision tree models with week 12 and earlier assessments that predicted long-term LDA. LDA, defined as disease activity score in 28 joints (DAS28) ≤3.2 or clinical disease activity index ≤10.0, was measured at 52 or 48 weeks. Demographics, laboratory data and clinical data at baseline and to week 12 were analysed as predictors of response.. ...
Methods and Results-One hundred twenty consecutive patients underwent CMR and CA. The etiology was ascribed by a consensus panel that used the results of the CMR scans. Similarly, a separate consensus group ascribed an underlying cause by using the results of CA. The diagnostic accuracy of both strategies was compared against a gold-standard panel that made a definitive judgment by reviewing all clinical data. The study was powered to show noninferiority between the 2 techniques. The sensitivity of 100%, specificity of 96%, and diagnostic accuracy of 97% for LGE-CMR were equivalent to CA (sensitivity, 93%; specificity, 96%; and diagnostic accuracy, 95%). As a gatekeeper to CA, LGE-CMR was also found to be a cheaper diagnostic strategy in a decision tree model when United Kingdom-based costs were assumed. The economic merits of this model would change, depending on the relative costs of LGE-CMR and CA in any specific healthcare system.. ...
Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors (e.g. chemistry development kit, CDK) and modeling algorithms, as this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of SMARTS keys had good statistics (Kappa = 0.43, sensitivity = 0.57, specificity 0.91, positive predicted value (PPV) = 0.64) equivalent to models built with commercial MOE2D and the same set of SMARTS keys (Kappa = 0.43, sensitivity = 0.58, specificity 0.91, PPV = 0.63). Extending the dataset to ~193,000 molecules and generating a continuous model using Cubist with a combination of CDK and ...
Beginning with the root, the tree will grow as we continue to split nodes. Since we wish to stop the splitting process when we have the best possible tree, we need some way to assess the quality of a decision tree.. Suppose we are constructing our decision tree and arrive at a node $t$. We may declare this node to be a leaf and assign all the cases in ${\cal L}(t)$ to a single class $j(t)$. Often, we choose $j(t)$ so that the smallest number of cases are misclassified, in which case $j(t)$ is the class having the largest number of cases in ${\cal L}(t)$.. Sometimes, however, some misclassifications are worse than others. For instance, in the study of heart attack patients described in the introduction, it is better to err on the side of caution by misclassifying a patient as at risk of dying within 30 days rather than misclassifying a patient that is truly at risk.. For this reason, we frequently introduce a misclassification cost $C_{ji}$ that describes the relative cost of misclassifying a ...
I have reviewed this guidance, including the decision tree below, and I am still not sure my research qualifies, who can I ask?. Send your question to [email protected] with the subject line COVID-19 and we will reply as soon as possible.. At this point the decision to keep a laboratory open should be based on following the decision tree at the bottom of this document and in close consultation with your department chair or director, and College or School.. Decision tree to determine whether your in-person research meets criteria for operation under the Stay Home, Stay Healthy directive. 1.Is your research allowable based on one or more of the exclusion criteria below?. Question 1a. Does your research fall into any of the above categories?. If yes, go to question 2.. Question 1b. Do you help support a facility that stores, analyzes, or otherwise processes samples, houses and/or carries out procedures with animals, or carries out computation?. If yes, go to question 2.. Question 1c. Do you support a ...
An interactive telephone answering system (20) connectable to a captured telephone line (26) is responsive to the detection (32, 34, 36, 38, 40, 42) of tones transmitted by the calling party from the keypad of a Touch-Tone phone for enabling caller selectable routing of an incoming call to a desired receiving phone (110) in accordance with a verbally interactive prerecorded decision tree format. The various selectable decision trees are prerecorded on a multitrack endless loop tape for playback (60, 62) of the channels selected (52, 54, 56, 58) by the calling party with the ultimate branches thereof each having a prerecorded arming signal which, when detected, activates the appropriate relay (50) to connect the caller to the desired telephone extension. The receiving party may also screen calls or have calls forwarded to another number by use of prerecorded codes on the tape. The decision tree format of the prerecorded messages is based on the recording of each channel in a specially created time
Japan Geoscience Union Meeting 2016,Classification and Regression Tree Analysis of the Relationship between the Yellow Dust Concentration and TOA Reflectance observed with GOSAT CAI Sensor
There is a need for a nonacceptable symptom state to indicate when a patient with knee or hip osteoarthritis exhibits symptoms severe enough to warrant total joint replacement (TJR). A previous study using logistic regression and ROC curve analysis was unable to determine pain and functional disability cut points leading to a TJR recommendation. Using the datasets from the previous study, classification trees were used to identify predictors and cut points of those predictors leading to a TJR recommendation. From the analysis, a patients quality of life and joint space narrowing appeared to be the most important predictors, out of those included in the analysis, of a surgeons recommendation for TJR. Further research and analysis is needed to determine if the generated classification trees accurately predict a surgeons recommendation for TJR ...
Diabetes is a chronic disease or group of metabolic disease where a person suffers from an extended level of blood glucose in the body, which is either the insulin production is inadequate, or because the bodys cells do not respond properly to insulin. The constant hyperglycemia of diabetes is related to long-haul harm, brokenness, and failure of various organs, particularly the eyes, kidneys, nerves, heart, and veins. The objective of this research is to make use of significant features, design a prediction algorithm using Machine learning and find the optimal classifier to give the closest result comparing to clinical outcomes. The proposed method aims to focus on selecting the attributes that ail in early detection of Diabetes Miletus using Predictive analysis. The result shows the decision tree algorithm and the Random forest has the highest specificity of 98.20% and 98.00%, respectively holds best for the analysis of diabetic data. Naïve Bayesian outcome states the best accuracy of 82.30%. The
Diabetes is a chronic disease or group of metabolic disease where a person suffers from an extended level of blood glucose in the body, which is either the insulin production is inadequate, or because the bodys cells do not respond properly to insulin. The constant hyperglycemia of diabetes is related to long-haul harm, brokenness, and failure of various organs, particularly the eyes, kidneys, nerves, heart, and veins. The objective of this research is to make use of significant features, design a prediction algorithm using Machine learning and find the optimal classifier to give the closest result comparing to clinical outcomes. The proposed method aims to focus on selecting the attributes that ail in early detection of Diabetes Miletus using Predictive analysis. The result shows the decision tree algorithm and the Random forest has the highest specificity of 98.20% and 98.00%, respectively holds best for the analysis of diabetic data. Naïve Bayesian outcome states the best accuracy of 82.30%. The
Mdl = TreeBagger(NumTrees,Tbl,ResponseVarName) returns an ensemble of NumTrees bagged classification trees trained using the sample data in the table Tbl.
Due to the continuous improvements of high throughput technologies and experimental procedures, the number of sequenced genomes is increasing exponentially. Ultimately, the task of annotating these data relies on the expertise of biologists. The necessity for annotation to be supervised by human experts is the rate limiting step of the data analysis. To face the deluge of new genomic data, the need for automating, as much as possible, the annotation process becomes critical. We consider annotation of a protein with terms of the functional hierarchy that has been used to annotate Bacillus subtilis and propose a set of rules that predict classes in terms of elements of the functional hierarchy, i.e., a class is a node or a leaf of the hierarchy tree. The rules are obtained through two decision-trees techniques: first-order decision-trees and multilabel attribute-value decision-trees, by using as training data the proteins from two lactic bacteria: Lactobacillus sakei and Lactobacillus bulgaricus. We
Video created by 密歇根大学 for the course Applied Machine Learning in Python. This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and ...
Department of Geomatics, University of Melbourne.. Decision trees have been applied to classify marine habitat types within the Recherché Archipelago, Western Australia. Known for their ease of interpretation and abilities to handle both numeric and categorical variables, decision trees can include both biological and spatial variables to classify and predict ecological relationships. The results of the classification are graphically presented as a tree outlining a series of rules, in the form of if-then statements, by which classes or groups are defined.. Input variables such as depth, relief and substrate were used initially to classify and predict habitat types, using a video survey of 2700 locations. Accuracies of 80% were achieved when the model was applied separately to predict habitat types of locations withheld from the training phase of the model. Future modelling will include additional variables already collected, such as species presence/absence data, to improve the classification ...
Case study link below: http://cdnfiles.laureate.net/2dett4d/Walden/NURS/6521/05/mm/decision_trees/week_07/index.html Write a 1- to 2-page summary paper that
Purpose The objective of this work was to determine which embryonic morphokinetic parameters up to D3 of in vitro development have predictive value for implantation for the selection of embryos for...
PubMed comprises more than 30 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.
Classification: 3 Step Process 1. Model construction (Learning): Each record (instance, example) is assumed to belong to a predefined class, as determined by one of the attributes This attribute is called the target attribute The values of the target attribute are the class labels The set of all instances used for learning the model is called training set 2. Model Evaluation (Accuracy): Estimate accuracy rate of the model based on a test set The known labels of test instances are compared with the predicts class from model Test set is independent of training set otherwise over-fitting will occur 3. Model Use (Classification): The model is used to classify unseen instances (i.e., to predict the class labels for new unclassified instances) Predict the value of an actual attribute
Table of statistics mentioned in the Decision Tree and programs which compute them. Statistics not listed here but mentioned in the Decision Tree are not produced by any of the three systems in the table.. ...
Relations between an dependent categorial variable and independent variables can be analyzed with logit models. The first part of the paper gives an short overview on different logit models including models for binary panel data, ordinal variables and decision trees. The availability of these model... mehr Relations between an dependent categorial variable and independent variables can be analyzed with logit models. The first part of the paper gives an short overview on different logit models including models for binary panel data, ordinal variables and decision trees. The availability of these models im BMDP, LIMDEP, SAS, SPSS, SYSTAT and the free ware statistical system TDA is discussed in the second part. Though only few procedures are designed especially to estimate the parameters of logistic models other procedures can be used as well. For example, the conditional logit model or logistic discrete choice model may be estimated by procedures for event history analysis. Exemplaric program ...
Regulatory Toxicology and Pharmacology. 2014;68:275-296. Thousands of man-made chemicals in use today may enter the food chain. Some of these may result in human exposure, while no toxicological data may be available. The present manuscript illustrates a decision tree for risk assessors and managers to establish fast and reliable safety levels of concern associated with dietary exposures.. Thousands of man-made chemicals are in use today. Many of these compounds may enter the food chain and result in human exposure. Since toxicological information is limited or lacking for the vast majority of these chemicals, the assessment of their health significance is therefore difficult, but envisaged.. A decision tree has been developed taking into account practical application of in silico methods. Basic concepts of classical risk assessment have been integrated: 1) exposure assessment 2) hazard identification 3) hazard characterization and 4) risk characterization.. The present work indicates that if ...