Machine Learning in Medicine - Cookbook Two

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.