Machine Learning in Medicine - Cookbook

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.

From the reviews:

"This is a concise, instructive and practical text on the various models of machine learning with particular reference to their applicability in medicine. ... The book is primarily aimed at students, health professionals and researchers with basic experience in statistics who are looking for a quick review prior to using machine learning tools. ... This book is a valuable resource for those who need a quick reference for machine learning models in medicine." (Kamesh Sivagnanam, Doody's Book Reviews, April, 2014)

From the reviews:

"This is a concise, instructive and practical text on the various models of machine learning with particular reference to their applicability in medicine. ... The book is primarily aimed at students, health professionals and researchers with basic experience in statistics who are looking for a quick review prior to using machine learning tools. ... This book is a valuable resource for those who need a quick reference for machine learning models in medicine." (Kamesh Sivagnanam, Doody's Book Reviews, April, 2014)