Machine Learning in Medicine: Part Three

ISBN-13: 9789402402605 / Angielski / Miękka / 2017 / 224 str.

Ton J. Cleophas; Aeilko H. Zwinderman

Opis

Offering sequenced guidance for non-specialists on how to reap the benefits of machine learning in medicine and healthcare, this text harnesses the power of cutting-edge computing to maximize the accessibility and analytic value of stored data and records.

Recenzje

From the reviews:

"This book is excellent. It is valuable source of a basic understanding of novel machine learning methods of clinical data analysis and can be used as a reference by students and teachers of epidemiology, statistics and biostatistics, computer and social scientists, and clinical investigators." (Vedang J. Bhavsar, Doody's Book Reviews, March, 2014)

Spis treści

Preface

1            Introduction to Machine Learning Part Three                                                                                                                                                               

2            Evolutionary Operations                                               

3             Multiple Treatments                                                                                        

4             Multiple Endpoints                                                                                          

5             Optimal Binning                                                                               

6             Exact P-Values                                                                                                 

7             Probit Regression                                                                                              

8             Over-dispersion                                                                                                

9             Random Effects                                                                                              

10           Weighted Least Squares                                                                                   

11           Multiple Response Sets                                                                                  

12           Complex Samples                                                                                            

13           Runs Tests                                                                                                        

14           Decision Trees                                                                                                   

15           Spectral Plots                                                                                                    

16           Newton's Methods                                                                                            

17           Stochastic Processes, Stationary Markov Chains                                     

18           Stochastic Processes, Absorbing Markov Chains                                      

19           Conjoint Models                                                                              

20           Machine Learning and Unsolved Questions                               

Index    

Blurb

Machine learning is concerned with the analysis of large data and multiple variables. It is also often more sensitive than traditional statistical methods to analyze small data. The first and second volumes reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, fuzzy modeling, various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, association rule learning, anomaly detection, and correspondence analysis. This third volume addresses more advanced methods and includes subjects like evolutionary programming, stochastic methods, complex sampling, optional binning, Newton's methods, decision trees, and other subjects. Both the theoretical bases and the step by step analyses are described for the benefit of non-mathematical readers. Each chapter can be studied without the need to consult other chapters. Traditional statistical tests are, sometimes, priors to machine learning methods, and they are also, sometimes, used as contrast tests. To those wishing to obtain more knowledge of them, we recommend to additionally study (1) Statistics Applied to Clinical Studies 5th Edition 2012, (2) SPSS for Starters Part One and Two 2012, and (3) Statistical Analysis of Clinical Data on a Pocket Calculator Part One and Two 2012, written by the same authors, and edited by Springer, New York.