Machine Learning in Medicine - Cookbook

Chapter 1 Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys (50 Patients)

Chapter 2 Density-Based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data (50 Patients)

Chapter 3 Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships in Individual Future Patients (120 Patients)

Chapter 4 Linear, Logistic, and Cox Regression for Outcome Prediction with Unpaired Data (20, 55, and 60 Patients)

Chapter 5 Generalized Linear Models for Outcome Prediction with Paired Data (100 Patients and 139 Physicians)

Chapter 6 Generalized Linear Models for Predicting Event-Rates (50 Patients)

Chapter 7 Factor Analysis and Partial Least Squares for Complex-Data Reduction (250 Patients)

Chapter 8 Optimal Scaling of High-Sensitivity Analysis of Health Predictors (250 Patients)

Chapter 9 Discriminant Analysis for Making a Diagnosis from Multiple Outcomes (45 Patients)

Chapter 10 Weighted Least Squares for Adjusting Efficacy Data with Inconsistent Spread (78 Patients)

Chapter 11 Partial Correlations for Removing Interaction Effects from Efficacy Data (64 Patients)

Chapter 12 Canonical Regression for Overall Statistics of Multivariate Data (250 Patients)

Chapter 13 Neural Networks for Assessing Relationships that are Typically Nonlinear (90 Patients)

Chapter 14 Complex Samples Methodologies for Unbiased Sampling (9,678 Persons)

Chapter 15 Correspondence Analysis for Identifying the Best of Multiple Treatments in Multiple Groups (217 Patients)

Chapter 16 Decision Trees for Decision Analysis (1,004 and 953 Patients)

Chapter 17 Multidimensional Scaling for Visualizing Experienced Drug Efficacies (14 Pain-Killers and 42 Patients)

Chapter 18 Stochastic Processes for Long Term Predictions from Short Term Observations

Chapter 19 Optimal Binning for Finding High Risk Cut-offs (1445 Families)

Chapter 20 Conjoint Analysis for Determining the Most Appreciated Properties of Medicines to be Developed (15 Physicians)