Machine Learning in Medicine - Cookbook Three
Unique features of the book involve the following. 1.This book is the third volume of a three volume series of cookbooks entitled "Machine Learning in Medicine - Cookbooks One, Two, and Three". No other self-assessment works for the medical and health care community covering the field of m...
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| Main Authors: | , |
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| Corporate Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2014.
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| Edition: | 1st ed. 2014. |
| Series: | SpringerBriefs in Statistics,
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-319-12163-5 |
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Table of Contents:
- Preface
- I. Cluster Models
- Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys.- Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data.- Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships
- II. Linear Models.- Linear, Logistic, and Cox Regression for Outcome Prediction with Unpaired Data.-Generalized Linear Models for Outcome Prediction with Paired Data.- Generalized Linear Models for Predicting Event-Rates.-Factor Analysis and Partial Least Squares (PLS) for Complex-Data Reduction
- Optimal Scaling of High-sensitivity Analysis of Health Predictors.- Discriminant Analysis for Making a Diagnosis from Multiple Outcomes.- Weighted Least Squares for Adjusting Efficacy Data with Inconsistent Spread.- Partial Correlations for Removing Interaction Effects from Efficacy Data.- Canonical Regression for Overall Statistics of Multivariate Data
- III. Rules Models
- Neural Networks for Assessing Relationships that are Typically Nonlinear.-Complex Samples Methodologies for Unbiased Sampling.-Correspondence Analysis for Identifying the Best of Multiple Treatments in Multiple Groups.- Decision Trees for Decision Analysis.- Multidimensional Scaling for Visualizing Experienced Drug Efficacies.- Stochastic Processes for Long Term Predictions from Short Term Observations.- Optimal Binning for Finding High Risk Cut-offs.- Conjoint Analysis for Determining the Most Appreciated Properties of Medicines to be Developed
- Index.