The authors have taught statistics and given statistics workshops in France and the Netherlands for almost 4 years by now. Their material, mainly on power point, consists of 12 lectures that have been continuously changed and improved by interaction with various audiences. For the purpose of the current book simple English text has been added to the formulas and figures, and the power points sheets have been rewritten in the format given by Kluwer Academic Publishers. Cartoons have been removed, since this is not so relevant for the transmission of thought through a written text, and at the...
The authors have taught statistics and given statistics workshops in France and the Netherlands for almost 4 years by now. Their material, mainly on p...
This book presents the latest developments in the field of clinical data analysis. It emphasizes non-classical but increasingly frequently used methods such as equivalence testing, interaction assessment and analysis of genetic data.
This book presents the latest developments in the field of clinical data analysis. It emphasizes non-classical but increasingly frequently used method...
Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection, correspondence analysis, and other...
Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statis...
This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings.
In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the...
This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to anal...
The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patterns in big data and data with multiple exposure / outcome variables and analysis-rules for surveys and questionnaires, currently common methods of data collection, are, essentially, missing.
Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning and the current 100 page cookbook should be helpful to that aim. It covers in a condensed...
The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data an...
Thanks to the omnipresent computer, current statistics can include data files of many thousands of values, and can perform any exploratory analysis in less than seconds. This development, however fascinating, generally does not lead to simple results. We should not forget that clinical studies are, mostly, for confirming prior hypotheses based on sound arguments, and the simplest tests provide the best power and are adequate for such studies. In the past few years the authors of this 5th edition, as teachers and research supervisors in academic and top-clinical facilities, have been able to...
Thanks to the omnipresent computer, current statistics can include data files of many thousands of values, and can perform any exploratory analysis in...
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 questionnaires, currently common methods of data collection, are, essentially, missing. Consequently, proper data-based health decisions will soon be impossible.
Obviously, it is time that medical and health professionals mastered their reluctance to use machine learning methods and this was the main...
The amount of data medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional data analysis has dif...
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 machine learning have been published to date.
2. Each chapter of the book can be studied without the need to consult other chapters, and can, for the readership's convenience, be downloaded from the internet. Self-assessment examples are available at extras.springer.com.
3. An adequate...
Unique features of the book involve the following.
1.This book is the third volume of a three volume series of cookbooks entitled "Machine Le...
Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are...
Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods...
Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection, correspondence analysis, and other...
Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statis...
