Machine Learning in Medicine - Cookbook Two
The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnair...
| 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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| Series: | SpringerBriefs in Statistics,
49 |
| Subjects: | |
| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- Preface. I Cluster models
- Nearest Neighbors for Classifying New Medicines
- Predicting High-Risk-Bin Memberships
- Predicting Outlier Memberships
- Linear Models
- Polynomial Regression for Outcome Categories
- Automatic Nonparametric Tests for Predictor Categories- Random Intercept Models for Both Outcome and Predictor
- Automatic Regression for Maximizing Linear Relationships
- Simulation Models for Varying Predictors
- Generalized Linear Mixed Models for Outcome Prediction from Mixed Data
- Two Stage Least Squares for Linear Models with Problematic
- Autoregressive Models for Longitudinal Data. II Rules Models
- Item Response Modeling for Analyzing Quality of Life with Better Precision
- Survival Studies with Varying Risks of Dying
- Fuzzy Logic for Improved Precision of Pharmacological Data Analysis
- Automatic Data Mining for the Best Treatment of a Disease
- Pareto Charts for Identifying the Main Factors of Multifactorial
- Radial Basis Neural Networks for Multidimensional Gaussian
- Automatic Modeling for Drug Efficacy Prediction
- Automatic Modeling for Clinical Event Prediction
- Automatic Newton Modeling in Clinical Pharmacology
- Index.
