Machine learning in medicine - cookbook two /
Ton J. Cleophas, Aeilko H. Zwinderman.
- xi, 140 páginas : 49 ilustraciones
- SpringerBriefs in Statistics, 49 2191-544X ; .
Springer eBooks
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.