Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Analysis of Safety Data of Drug Trials [[electronic resource] ] : An Update / / by Ton J. Cleophas, Aeilko H. Zwinderman
| Analysis of Safety Data of Drug Trials [[electronic resource] ] : An Update / / by Ton J. Cleophas, Aeilko H. Zwinderman |
| Autore | Cleophas Ton J |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XI, 217 p. 191 illus., 28 illus. in color.) |
| Disciplina | 615.19 |
| Soggetto topico |
Medicine
Statistics Biomedicine, general Statistics and Computing/Statistics Programs |
| ISBN | 3-030-05804-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- General Introduction -- Significant and Insignificant Adverse Effect -- Incidence Ratios and Reporting Ratios of Adverse Effects -- Safety Analysis and the Alternative Hypothesis -- Forest Plots of Adverse Effects -- Graphics of Adverse Effects -- Repeated Measures Methods for Testing Adverse Effects -- Benefit Risk Ratios -- Equivalence, Non-inferiority and Superiority Testing of Adverse Effects -- Part II The Analysis of Dependent Adverse Effects -- Independent and Dependent Adverse Effects. Categorical Predictors Assessed as Dependent Adverse Effects. Adverse Effect of the Dependent Type in Crossover Trial -- Confoundings and Interactions Assessed as Dependent Adverse Effects -- Subgroup Characteristics Assessed as Dependent Adverse Effects -- Random Effects Assessed as Dependent Adverse Effects -- Outliers Assessed as Dependent Adverse Effects -- Index. . |
| Record Nr. | UNINA-9910337945403321 |
Cleophas Ton J
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Clinical Data Analysis on a Pocket Calculator [[electronic resource] ] : Understanding the Scientific Methods of Statistical Reasoning and Hypothesis Testing / / by Ton J. Cleophas, Aeilko H. Zwinderman
| Clinical Data Analysis on a Pocket Calculator [[electronic resource] ] : Understanding the Scientific Methods of Statistical Reasoning and Hypothesis Testing / / by Ton J. Cleophas, Aeilko H. Zwinderman |
| Autore | Cleophas Ton J |
| Edizione | [2nd ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XXIII, 334 p. 65 illus., 41 illus. in color.) |
| Disciplina | 610.727 |
| Soggetto topico |
Medicine
Entomology Pharmacy Statistics Biomedicine, general Statistics for Life Sciences, Medicine, Health Sciences |
| ISBN | 3-319-27104-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface.-I Continuous Outcome Data -- Data Spread, Standard Deviations -- Data Summaries: Histograms, Wide and Narrow Gaussian Curves -- Null-Hypothesis Testing with Graphs -- Null-Hypothesis Testing with the T-table -- One-Sample Continuous Data (One-Sample T-Test, One-Sample Wilcoxon -- Paired Continuous Data (Paired T-Test, Two-Sample Wilcoxon Signed Rank Test) -- Unpaired Continuous Data (Unpaired T-Test, Mann-Whitney) -- Linear Regression (Regression Coefficients, Correlation Coefficients, and their Standard Errors) -- Kendall-Tau Regression for Ordinal Data -- Paired Continuous Data, Analysis with Help of Correlation Coefficients -- Power Equations -- Sample Size Calculations -- Confidence Intervals -- Equivalence Testing instead of Null-Hypothesis Testing -- Noninferiority Testing instead of Null-Hypothesis Testing -- Superiority Testing instead of Null-Hypothesis Testing -- Missing Data Imputation -- Bonferroni Adjustments -- Unpaired Analysis of Variance (ANOVA) -- Paired Analysis of Variance (ANOVA).-Variability Analysis for One or Two Samples -- 22 Variability Analysis for Three or More Samples -- Confounding -- Propensity Score and Propensity Score Matching for Multiple Confounders -- Interaction -- Accuracy and Reliability Assessments -- Robust Tests for Imperfect Data -- Non-linear Modeling on a Pocket Calculator -- Fuzzy Modeling for Imprecise and Incomplete Data -- Bhattacharya Modeling for Unmasking Hidden Gaussian Curves -- Item Response Modeling instead of Classical Linear Analysis of Questionnaires -- Meta-Analysis -- Goodness of Fit Tests for Identifying Nonnormal Data -- Non-Parametric Tests for Three or More Samples (Friedman and Kruskal-Wallis) -- II Binary Outcome Data.-Data Spread: Standard Deviation, One Sample Z- Test, One Sample Binomial Test -- Z-Tests -- Phi Tests for Nominal Data -- 38 Chi-Square Tests -- Fisher Exact Tests Convenient for Small Samples -- Confounding -- Interaction -- Chi-square Tests for Large Cross-Tabs -- Logarithmic Transformations, a Great Help to Statistical Analyses -- Odds Ratios, a Short-Cut for Analyzing Cross-Tabs -- Log odds, the Basis of Logistic Regression -- Log Likelihood Ratio Tests for the Best Precision -- Hierarchical Loglinear Models for Higher Order Cross-Tabs -- McNemar Tests for Paired Cross-Tabs -- McNemar Odds Ratios -- Power Equations -- Sample Size Calculations -- Accuracy Assessments -- Reliability Assessments -- Unmasking Fudged Data -- Markov Modeling for Predictions outside the Range of Observations -- Binary Partitioning with CART (Classification and Regression Tree) Methods -- Meta-Analysis -- Physicians' Daily Life and the Scientific Method -- Incident Analysis and the Scientific Method -- Cochran Tests for Large Paired Cross-Tabs.-Index. . |
| Record Nr. | UNINA-9910253872103321 |
|
Cleophas Ton J
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Efficacy Analysis in Clinical Trials an Update [[electronic resource] ] : Efficacy Analysis in an Era of Machine Learning / / by Ton J. Cleophas, Aeilko H. Zwinderman
| Efficacy Analysis in Clinical Trials an Update [[electronic resource] ] : Efficacy Analysis in an Era of Machine Learning / / by Ton J. Cleophas, Aeilko H. Zwinderman |
| Autore | Cleophas Ton J |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XI, 304 p. 295 illus., 44 illus. in color.) |
| Disciplina | 006.31 |
| Soggetto topico |
Medicine
Statistics Biostatistics Biomedicine, general Statistics for Life Sciences, Medicine, Health Sciences |
| ISBN | 3-030-19918-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Traditional and Machine-Learning Methods for Efficacy Analysis -- Optimal-Scaling for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Ratio-Statistic for Efficacy Analysis -- Complex-Samples for Efficacy Analysis -- Bayesian-Networks for Efficacy Analysis -- Evolutionary-Operations for Efficacy Analysis -- Automatic-Newton-Modeling for Efficacy Analysis -- High-Risk-Bins for Efficacy Analysis -- Balanced-Iterative-Reducing-Hierarchy for Efficacy Analysis -- Cluster-Analysis for Efficacy Analysis -- Multidimensional-Scaling for Efficacy Analysis -- Binary Decision-Trees for Efficacy Analysis -- Continuous Decision-Trees for Efficacy Analysis -- Automatic-Data-Mining for Efficacy Analysis -- Support-Vector-Machines for Efficacy Analysis -- Neural-Networks for Efficacy Analysis -- Ensembled-Accuracies for Efficacy Analysis -- Ensembled-Correlations for Efficacy Analysis -- Gamma-Distributions for Efficacy Analysis -- Validation with Big Data, a Big Issue -- Index. |
| Record Nr. | UNINA-9910349444503321 |
|
Cleophas Ton J
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning in Medicine [[electronic resource] ] : Part Three / / by Ton J. Cleophas, Aeilko H. Zwinderman
| Machine Learning in Medicine [[electronic resource] ] : Part Three / / by Ton J. Cleophas, Aeilko H. Zwinderman |
| Autore | Cleophas Ton J |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (XIX, 224 p. 41 illus.) |
| Disciplina | 610.28563 |
| Soggetto topico |
Medicine
Statistics Optical data processing Biomedicine, general Medicine/Public Health, general Statistics, general Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 94-007-7869-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Introduction to Machine Learning Part Three.- Evolutionary Operations.- Multiple Treatments -- Multiple Endpoints -- Optimal Binning -- Exact P-Values -- Probit Regression -- Over - dispersion.10 Random Effects -- Weighted Least Squares -- Multiple Response Sets -- Complex Samples -- Runs Tests.- Decision Trees -- Spectral Plots -- Newton's Methods -- Stochastic Processes, Stationary Markov Chains -- Stochastic Processes, Absorbing Markov Chains -- Conjoint Models -- Machine Learning and Unsolved Questions -- Index. |
| Record Nr. | UNINA-9910437846303321 |
|
Cleophas Ton J
|
||
| Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning in Medicine [[electronic resource] ] : Part Two / / by Ton J. Cleophas, Aeilko H. Zwinderman
| Machine Learning in Medicine [[electronic resource] ] : Part Two / / by Ton J. Cleophas, Aeilko H. Zwinderman |
| Autore | Cleophas Ton J |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (234 p.) |
| Disciplina | 610.285 |
| Soggetto topico |
Medicine
Entomology Statistics Optical data processing Literacy Biomedicine, general Medicine/Public Health, general Statistics, general Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 94-007-6886-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Machine Learning Part Two -- Two-stage Least Squares -- Multiple Imputations -- Bhattacharya Analysis -- Quality-of-life (QOL) Assessments with Odds Ratios -- Logistic Regression for Assessing Novel Diagnostic Tests against Control -- Validating Surrogate Endpoints -- Two-dimensional Clustering -- Multidimensional Clustering -- Anomaly Detection -- Association Rule Analysis -- Multidimensional Scaling -- Correspondence Analysis -- Multivariate Analysis of Time Series -- Support Vector Machines -- Bayesian Networks -- Protein and DNA Sequence Mining -- Continuous Sequential Techniques -- Discrete Wavelet Analysis -- Machine Learning and Common Sense -- Statistical Tables -- Index. |
| Record Nr. | UNINA-9910437826503321 |
|
Cleophas Ton J
|
||
| Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning in Medicine [[electronic resource] /] / by Ton J. Cleophas, Aeilko H. Zwinderman
| Machine Learning in Medicine [[electronic resource] /] / by Ton J. Cleophas, Aeilko H. Zwinderman |
| Autore | Cleophas Ton J |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (270 p.) |
| Disciplina | 610.28563 |
| Soggetto topico |
Medicine
Entomology Statistics Optical data processing Literacy Biomedicine, general Medicine/Public Health, general Statistics, general Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 94-007-5824-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- 1 Introduction to machine learning -- 2 Logistic regression for health profiling -- 3 Optimal scaling: discretization -- 4 Optimal scaling: regularization including ridge, lasso, and elastic net regression -- 5 Partial correlations -- 6 Mixed linear modelling -- 7 Binary partitioning -- 8 Item response modelling -- 9 Time-dependent predictor modelling -- 10 Seasonality assessments -- 11 Non-linear modelling -- 12 Artificial intelligence, multilayer Perceptron modelling -- 13 Artificial intelligence, radial basis function modelling -- 14 Factor analysis -- 15 Hierarchical cluster analysis for unsupervised data -- 16 Partial least squares -- 17 Discriminant analysis for Supervised data -- 18 Canonical regression -- 19 Fuzzy modelling -- 20 Conclusions. Index. . |
| Record Nr. | UNINA-9910437844503321 |
|
Cleophas Ton J
|
||
| Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning in Medicine - a Complete Overview [[electronic resource] /] / by Ton J. Cleophas, Aeilko H. Zwinderman
| Machine Learning in Medicine - a Complete Overview [[electronic resource] /] / by Ton J. Cleophas, Aeilko H. Zwinderman |
| Autore | Cleophas Ton J |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XXIV, 516 p. 159 illus.) |
| Disciplina | 006.31 |
| Soggetto topico |
Medicine
Statistics Biomedicine, general Medicine/Public Health, general Statistics, general Science, Humanities and Social Sciences, multidisciplinary |
| ISBN | 3-319-15195-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface. Section I Cluster and Classification Models -- Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys (50 Patients) -- Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data (50 Patients) -- Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships in Individual Future Patients (120 Patients)- Nearest Neighbors for Classifying New Medicines (2 New and 25 Old Opioids)- Predicting High-Risk-Bin Memberships (1445 Families) -- Predicting Outlier Memberships (2000 Patients) -- Data Mining for Visualization of Health Processes (150 Patients) -- 8 Trained Decision Trees for a More Meaningful Accuracy (150 Patients) -- Typology of Medical Data (51 Patients) -- Predictions from Nominal Clinical Data (450 Patients) -- Predictions from Ordinal Clinical Data (450 Patients) -- Assessing Relative Health Risks (3000 Subjects) -- Measurement Agreements (30 Patients) -- Column Proportions for Testing Differences between Outcome Scores (450 Patients) -- Pivoting Trays and Tables for Improved Analysis of Multidimensional Data (450 Patients) -- Online Analytical Procedure Cubes for a More Rapid Approach to Analyzing Frequencies (450 Patients) -- Restructure Data Wizard for Data Classified the Wrong Way (20 Patients).- Control Charts for Quality Control of Medicines (164 Tablet Disintegration Times) -- Section II (Log) Linear Models -- Linear, Logistic, and Cox Regression for Outcome Prediction with Unpaired Data (20, 55, and 60 Patients).- Generalized Linear Models for Outcome Prediction with Paired Data (100 Patients and 139 Physicians) -- Generalized Linear Models for Predicting Event-Rates (50 Patients).- Factor Analysis and Partial Least Squares (PLS) for Complex-Data Reduction (250 Patients) -- Optimal Scaling of High-sensitivity Analysis of Health Predictors (250 Patients) -- Discriminant Analysis for Making a Diagnosis from Multiple Outcomes (45 Patients) -- Weighted Least Squares for Adjusting Efficacy Data with Inconsistent Spread (78 Patients) -- Partial Correlations for Removing Interaction Effects from Efficacy Data (64 Patients) -- Canonical Regression for Overall Statistics of Multivariate Data (250 Patients) -- Multinomial Regression for Outcome Categories (55 Patients) -- Various Methods for Analyzing Predictor Categories (60 and 30 Patients) -- Random Intercept Models for Both Outcome and Predictor Categories (55 Patients).- Automatic Regression for Maximizing Linear Relationships (55 Patients) -- Simulation Models for Varying Predictors (9000 Patients) -- Generalized Linear Mixed Models for Outcome Prediction from Mixed Data (20 Patients) -- Two Stage Least Squares for Linear Models with Problematic Predictors (35 Patients) -- Autoregressive Models for Longitudinal Data (120 Monthly Population Records) -- Variance Components for Assessing the Magnitude of Random Effects (40 Patients) -- Ordinal Scaling for Clinical Scores with Inconsistent Intervals (900 Patients) -- Loglinear Models for Assessing Incident Rates with Varying Incident Risks (12 Populations).- Loglinear Models for Outcome Categories (445 Patients) -- Heterogeneity in Clinical Research: Mechanisms Responsible (20 Studies) -- Performance Evaluation of Novel Diagnostic Tests (650 and 588 Patients).- Quantile - Quantile Plots, a Good Start for Looking at Your Medical Data (50 Cholesterol Measurements and 52 Patients) -- Rate Analysis of Medical Data Better than Risk Analysis (52 Patients) -- Trend Tests Will Be Statistically Significant if Traditional Tests Are not (30 and 106 Patients) -- Doubly Multivariate Analysis of Variance for Multiple Observations from Multiple Outcome Variables (16 Patients) -- Probit Models for Estimating Effective Pharmacological Treatment Dosages (14 Tests) -- Interval Censored Data Analysis for Assessing Mean Time to Cancer Relapse (51 Patients).- Structural Equation Modeling with SPSS Analysis of Moment Structures (Amos) for Cause Effect Relationships I (35 Patients) -- Structural Equation Modeling with SPSS Analysis of Moment Structures (Amos) for Cause Effect Relationships II (35 Patients) -- Section III Rules Models -- Neural Networks for Assessing Relationships that are Typically Nonlinear (90 Patients). Complex Samples Methodologies for Unbiased Sampling (9,678 Persons) -- Correspondence Analysis for Identifying the Best of Multiple Treatments in Multiple Groups (217 Patients) -- Decision Trees for Decision Analysis (1004 and 953 Patients).-Multidimensional Scaling for Visualizing Experienced Drug Efficacies (14 Pain-killers and 42 Patients) -- Stochastic Processes for Long Term Predictions from Short Term Observations -- Optimal Binning for Finding High Risk Cut-offs (1445 Families).- Conjoint Analysis for Determining the Most Appreciated Properties of Medicines to Be Developed (15 Physicians) -- Item Response Modeling for Analyzing Quality of Life with Better Precision (1000 Patients) -- Survival Studies with Varying Risks of Dying (50 and 60 Patients) -- Fuzzy Logic for Improved Precision of Pharmacological Data Analysis (9 Induction Dosages) -- Automatic Data Mining for the Best Treatment of a Disease (90 Patients) -- Pareto Charts for Identifying the Main Factors of Multifactorial Outcomes (2000 Admissions to Hospital) -- Radial Basis Neural Networks for Multidimensional Gaussian Data (90 persons) -- Automatic Modeling for Drug Efficacy Prediction (250 Patients) -- Automatic Modeling for Clinical Event Prediction (200 Patients) -- Automatic Newton Modeling in Clinical Pharmacology (15 Alfentanil dosages, 15 Quinidine time-concentration relationships) -- Spectral Plots for High Sensitivity Assessment of Periodicity (6 Years’ Monthly C Reactive Protein Levels) -- Runs Test for Identifying Best Analysis Models (21 Estimates of Quantity and Quality of Patient Care) -- Evolutionary Operations for Health Process Improvement (8 Operation Room Settings).- Bayesian Networks for Cause Effect Modeling (600 Patients) -- Support Vector Machines for Imperfect Nonlinear Data -- Multiple Response Sets for Visualizing Clinical Data Trends (811 Patient Visits) -- Protein and DNA Sequence Mining -- Iteration Methods for Crossvalidation (150 Patients) -- Testing Parallel-groups with Different Sample Sizes and Variances (5 Parallel-group Studies) -- Association Rules between Exposure and Outcome (50 and 60 Patients) -- Confidence Intervals for Proportions and Differences in Proportions (100 and 75 Patients) -- Ratio Statistics for Efficacy Analysis of New Drugs 50 Patients).- Fifth Order Polynomes of Circadian Rhythms (1 Patient) -- Gamma Distribution for Estimating the Predictors of Medical Outcomes (110 Patients) Index. |
| Record Nr. | UNINA-9910298275303321 |
|
Cleophas Ton J
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning in Medicine - Cookbook [[electronic resource] /] / by Ton J. Cleophas, Aeilko H. Zwinderman
| Machine Learning in Medicine - Cookbook [[electronic resource] /] / by Ton J. Cleophas, Aeilko H. Zwinderman |
| Autore | Cleophas Ton J |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (131 pages) : illustrations |
| Disciplina | 006.31 |
| Collana | SpringerBriefs in Statistics |
| Soggetto topico |
Medicine
Biostatistics Statistics Application software Biometrics (Biology) Medicine/Public Health, general Statistics for Life Sciences, Medicine, Health Sciences Computer Applications Biometrics |
| ISBN |
1-306-54344-4
3-319-04181-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | I Cluster Models -- Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys (50 Patients) -- Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data (50 Patients) -- Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships in Individual Future Patients (120 Patients) -- II Linear Models -- Linear, Logistic and Cox Regression for Outcome Prediction with Unpaired Data (20, 55 and 60 Patients) -- Generalized Linear Models for Outcome Prediction with Paired Data (100 Patients and 139 Physicians) -- Generalized Linear Models for Predicting Event-Rates (50 Patients) Exact P-Values -- Factor Analysis and Partial Least Squares (PLS) for Complex-Data Reduction (250 Patients) -- Optimal Scaling of High-sensitivity Analysis of Health Predictors (250 Patients) -- Discriminant Analysis for Making a Diagnosis from Multiple Outcomes (45 Patients) -- Weighted Least Squares for Adjusting Efficacy Data with Inconsistent Spread (78 Patients) -- Partial Correlations for Removing Interaction Effects from Efficacy Data (64 Patients) -- Canonical Regression for Overall Statistics of Multivariate Data (250 Patients). III Rules Models -- Neural Networks for Assessing Relationships that are Typically Nonlinear (90 Patients) -- Complex Samples Methodologies for Unbiased Sampling (9,678 Persons) -- Correspondence Analysis for Identifying the Best of Multiple Treatments in Multiple Groups (217 Patients) -- Decision Trees for Decision Analysis (1004 and 953 Patients) -- Multidimensional Scaling for Visualizing Experienced Drug Efficacies (14 Pain-killers and 42 Patients) -- Stochastic Processes for Long Term Predictions from Short Term Observations -- Optimal Binning for Finding High Risk Cut-offs (1445 Families) -- Conjoint Analysis for Determining the Most Appreciated Properties of Medicines to Be Developed (15 Physicians) -- Index. |
| Record Nr. | UNINA-9910300151503321 |
|
Cleophas Ton J
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning in Medicine - Cookbook Three [[electronic resource] /] / by Ton J. Cleophas, Aeilko H. Zwinderman
| Machine Learning in Medicine - Cookbook Three [[electronic resource] /] / by Ton J. Cleophas, Aeilko H. Zwinderman |
| Autore | Cleophas Ton J |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (132 p.) |
| Disciplina |
004
004.0151 519.5 610 |
| Collana | SpringerBriefs in Statistics |
| Soggetto topico |
Medicine
Application software Computer science—Mathematics Statistics Biomedicine, general Computer Applications Medicine/Public Health, general Mathematics of Computing Statistics and Computing/Statistics Programs |
| ISBN | 3-319-12163-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- I. Cluster Models -- Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys.- Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data.- Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships -- II. Linear Models.- Linear, Logistic, and Cox Regression for Outcome Prediction with Unpaired Data.-Generalized Linear Models for Outcome Prediction with Paired Data.- Generalized Linear Models for Predicting Event-Rates.-Factor Analysis and Partial Least Squares (PLS) for Complex-Data Reduction -- Optimal Scaling of High-sensitivity Analysis of Health Predictors.- Discriminant Analysis for Making a Diagnosis from Multiple Outcomes.- Weighted Least Squares for Adjusting Efficacy Data with Inconsistent Spread.- Partial Correlations for Removing Interaction Effects from Efficacy Data.- Canonical Regression for Overall Statistics of Multivariate Data -- III. Rules Models -- Neural Networks for Assessing Relationships that are Typically Nonlinear.-Complex Samples Methodologies for Unbiased Sampling.-Correspondence Analysis for Identifying the Best of Multiple Treatments in Multiple Groups.- Decision Trees for Decision Analysis.- Multidimensional Scaling for Visualizing Experienced Drug Efficacies.- Stochastic Processes for Long Term Predictions from Short Term Observations.- Optimal Binning for Finding High Risk Cut-offs.- Conjoint Analysis for Determining the Most Appreciated Properties of Medicines to be Developed -- Index. |
| Record Nr. | UNINA-9910299986603321 |
|
Cleophas Ton J
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning in Medicine - Cookbook Two [[electronic resource] /] / by Ton J. Cleophas, Aeilko H. Zwinderman
| Machine Learning in Medicine - Cookbook Two [[electronic resource] /] / by Ton J. Cleophas, Aeilko H. Zwinderman |
| Autore | Cleophas Ton J |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (137 pages) : illustrations, tables |
| Disciplina | 610.28563 |
| Collana | SpringerBriefs in Statistics |
| Soggetto topico |
Medicine
Biostatistics Statistics Application software Biometrics (Biology) Medicine/Public Health, general Statistics for Life Sciences, Medicine, Health Sciences Computer Applications Biometrics |
| ISBN | 3-319-07413-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface. I Cluster models -- Nearest Neighbors for Classifying New Medicines -- Predicting High-Risk-Bin Memberships -- Predicting Outlier Memberships -- Linear Models -- Polynomial Regression for Outcome Categories -- Automatic Nonparametric Tests for Predictor Categories- Random Intercept Models for Both Outcome and Predictor -- Automatic Regression for Maximizing Linear Relationships -- Simulation Models for Varying Predictors -- Generalized Linear Mixed Models for Outcome Prediction from Mixed Data -- Two Stage Least Squares for Linear Models with Problematic -- Autoregressive Models for Longitudinal Data. II Rules Models -- Item Response Modeling for Analyzing Quality of Life with Better Precision -- Survival Studies with Varying Risks of Dying -- Fuzzy Logic for Improved Precision of Pharmacological Data Analysis -- Automatic Data Mining for the Best Treatment of a Disease -- Pareto Charts for Identifying the Main Factors of Multifactorial -- Radial Basis Neural Networks for Multidimensional Gaussian -- Automatic Modeling for Drug Efficacy Prediction -- Automatic Modeling for Clinical Event Prediction -- Automatic Newton Modeling in Clinical Pharmacology -- Index. |
| Record Nr. | UNINA-9910299961303321 |
|
Cleophas Ton J
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
