Machine Learning in Medicine - Cookbook

Cluster Models

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

2 Density-based Clustering to Identify Outlier Groups in Otherwise

Homogeneous Data (50 Patients)

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

Linear Models

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

5 Generalized Linear Models for Outcome Prediction with Paired

Data (100 Patients and 139 Physicians)

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

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

8 Optimal Scaling of High-sensitivity Analysis of Health Predictors

(250 Patients)

9 Discriminant Analysis for Making a Diagnosis from

Multiple Outcomes (45 Patients)

Inconsistent Spread (78 Patients)

11 Partial Correlations for Removing Interaction Effects from

Efficacy Data (64 Patients)

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

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

14 Complex Samples Methodologies for Unbiased Sampling

(9,678 Persons)

16 Decision Trees for Decision Analysis (1004 and 953 Patients)

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

18 Stochastic Processes for Long Term Predictions from Short

Term Observations

20 Conjoint Analysis for Determining the Most Appreciated

Properties of Medicines to Be Developed (15 Physicians)

Index