Electronic health records of modern health facilities, are increasingly complex and systematic assessment of these records is virtually impossible without special computationally intensive methods
Clinicians and other health professionals are not familiar with these methods, and this book is the first publication that systematically reviews such methods, particularly, for this audience
The book is written as a hand-hold presentation also accessible to non-mathematicians, and as a must-read publication for those new to the methods
The book includes step by step data analyses in SPSS, and can, therefore, also be used as a cookbook-like guide for those starting with the novel methodologies machine learning
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.
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
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)
Machine Learning in Medicine
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.
Preface.- Introduction to Machine Learning Part Three.- Evolutionary Operations.- Multiple Treatments.- Multiple Endpoints.- Optimal Binning.- Exact P-Values.- Probit Regression.- Over - dispersion.10 Random Effects.- Weighted Least Squares.- Multiple Response Sets.- Complex Samples.- Runs Tests.- Decision Trees.- Spectral Plots.- Newton's Methods.- Stochastic Processes, Stationary Markov Chains.- Stochastic Processes, Absorbing Markov Chains.- Conjoint Models.- Machine Learning and Unsolved Questions.- Index.
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)
Inhaltsverzeichnis
Preface.- Introduction to Machine Learning Part Three.- Evolutionary Operations.- Multiple Treatments.- Multiple Endpoints.- Optimal Binning.- Exact P-Values.- Probit Regression.- Over - dispersion.10 Random Effects.- Weighted Least Squares.- Multiple Response Sets.- Complex Samples.- Runs Tests.- Decision Trees.- Spectral Plots.- Newton's Methods.- Stochastic Processes, Stationary Markov Chains.- Stochastic Processes, Absorbing Markov Chains.- Conjoint Models.- Machine Learning and Unsolved Questions.- Index.
Klappentext
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.
Electronic health records of modern health facilities, are increasingly complex and systematic assessment of these records is virtually impossible without special computationally intensive methods
Clinicians and other health professionals are not familiar with these methods, and this book is the first publication that systematically reviews such methods, particularly, for this audience
The book is written as a hand-hold presentation also accessible to non-mathematicians, and as a must-read publication for those new to the methods
The book includes step by step data analyses in SPSS, and can, therefore, also be used as a cookbook-like guide for those starting with the novel methodologies machine learning
