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Author:   Ton J. Cleophas ,  Aeilko H. Zwinderman
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2014 ed.
Dimensions:   Width: 15.50cm , Height: 0.80cm , Length: 23.50cm
Weight:   0.454kg
ISBN:  

9783319121628

ISBN 10:   3319121626
Pages:   131
Publication Date:   10 November 2014
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Preface I. Cluster Models 1. Hierarchical Clustering and K-means Clustering to Identify Subgroups in Surveys 2. Density-based Clustering to Identify Outlier Groups in Otherwise Homogeneous Data 3. Two Step Clustering to Identify Subgroups and Predict Subgroup Memberships II. Linear Models 4. Linear, Logistic, and Cox Regression for Outcome Prediction with Unpaired Data 5. Generalized Linear Models for Outcome Prediction with Paired Data 6. Generalized Linear Models for Predicting Event-Rates 7. Factor Analysis and Partial Least Squares (PLS) for Complex-Data Reduction 8. Optimal Scaling of High-sensitivity Analysis of Health Predictors 9. Discriminant Analysis for Making a Diagnosis from Multiple Outcomes 10. Weighted Least Squares for Adjusting Efficacy Data with Inconsistent Spread 11. Partial Correlations for Removing Interaction Effects from Efficacy Data 12. Canonical Regression for Overall Statistics of Multivariate Data III. Rules Models 13. Neural Networks for Assessing Relationships that are Typically Nonlinear 14. Complex Samples Methodologies for Unbiased Sampling 15. Correspondence Analysis for Identifying the Best of Multiple Treatments in Multiple Groups 16. Decision Trees for Decision Analysis 17. Multidimensional Scaling for Visualizing Experienced Drug Efficacies 18. Stochastic Processes for Long Term Predictions from Short Term Observations 19. Optimal Binning for Finding High Risk Cut-offs 20. Conjoint Analysis for Determining the Most Appreciated Properties of Medicines to be Developed Index.

Reviews

From the book reviews: It provides a rapid review of tools used in machine learning that were not covered in in the first two cookbooks. The audience includes students, health professionals, and researchers with basic experience in statistics who are looking for a quick review prior to using machine learning tools. ... This is a valuable resource for those who need a quick reference on machine learning models in medicine. (Pooja Sethi, Doody's Book Reviews, February, 2015)

From the book reviews: It provides a rapid review of tools used in machine learning that were not covered in in the first two cookbooks. The audience includes students, health professionals, and researchers with basic experience in statistics who are looking for a quick review prior to using machine learning tools. ... This is a valuable resource for those who need a quick reference on machine learning models in medicine. (Pooja Sethi, Doody's Book Reviews, February, 2015)

From the book reviews: “It provides a rapid review of tools used in machine learning that were not covered in in the first two cookbooks. The audience includes students, health professionals, and researchers with basic experience in statistics who are looking for a quick review prior to using machine learning tools. … This is a valuable resource for those who need a quick reference on machine learning models in medicine.” (Pooja Sethi, Doody’s Book Reviews, February, 2015)

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