Machine Learning in Medicine - Cookbook

Authors:

ISBN: 978-3-319-04180-3 (Print) 978-3-319-04181-0 (Online)

Table of contents (20 chapters)

  1. Front Matter

    Pages i-xi

  2. Cluster Models

    1. Front Matter

      Pages 1-1

    2. No Access

      Chapter

      Pages 3-8

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

    3. No Access

      Chapter

      Pages 9-11

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

    4. No Access

      Chapter

      Pages 13-15

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

  3. Linear Models

    1. Front Matter

      Pages 17-17

    2. No Access

      Chapter

      Pages 19-27

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

    3. No Access

      Chapter

      Pages 29-35

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

    4. No Access

      Chapter

      Pages 37-41

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

    5. No Access

      Chapter

      Pages 43-49

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

    6. No Access

      Chapter

      Pages 51-56

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

    7. No Access

      Chapter

      Pages 57-61

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

    8. No Access

      Chapter

      Pages 63-66

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

    9. No Access

      Chapter

      Pages 67-71

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

    10. No Access

      Chapter

      Pages 73-77

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

  4. Rules Models

    1. Front Matter

      Pages 79-79

    2. No Access

      Chapter

      Pages 81-83

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

    3. No Access

      Chapter

      Pages 85-90

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

    4. No Access

      Chapter

      Pages 91-95

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

    5. No Access

      Chapter

      Pages 97-104

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

    6. No Access

      Chapter

      Pages 105-113

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

    7. No Access

      Chapter

      Pages 115-121

      Stochastic Processes for Long Term Predictions from Short Term Observations

    8. No Access

      Chapter

      Pages 123-127

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

    9. No Access

      Chapter

      Pages 129-134

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

  5. Back Matter

    Pages 135-137