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Machine Learning In Medicine A Complete Overview

Author: Ton J. Cleophas
Publisher: Springer
ISBN: 3319151959
Size: 29.89 MB
Format: PDF, ePub
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The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care. In eighty chapters eighty different machine learning methodologies are reviewed, in combination with data examples for self-assessment. Each chapter can be studied without the need to consult other chapters. The amount of data stored in the world's databases doubles every 20 months, and clinicians, familiar with traditional statistical methods, are at a loss to analyze them. Traditional methods have, indeed, difficulty to identify outliers in large datasets, and to find patterns in big data and data with multiple exposure / outcome variables. In addition, analysis-rules for surveys and questionnaires, which are currently common methods of data collection, are, essentially, missing. Fortunately, the new discipline, machine learning, is able to cover all of these limitations. So far medical professionals have been rather reluctant to use machine learning. Also, in the field of diagnosis making, few doctors may want a computer checking them, are interested in collaboration with a computer or with computer engineers. Adequate health and health care will, however, soon be impossible without proper data supervision from modern machine learning methodologies like cluster models, neural networks and other data mining methodologies. Each chapter starts with purposes and scientific questions. Then, step-by-step analyses, using data examples, are given. Finally, a paragraph with conclusion, and references to the corresponding sites of three introductory textbooks, previously written by the same authors, is given.

Regression Analysis In Medical Research

Author: Ton J. Cleophas
Publisher: Springer
ISBN: 3319719378
Size: 74.75 MB
Format: PDF, Docs
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This edition is a pretty complete textbook and tutorial for medical and health care students, as well as a recollection/update bench, and help desk for professionals. Novel approaches already applied in published clinical research will be addressed: matrix analyses, alpha spending, gate keeping, kriging, interval censored regressions, causality regressions, canonical regressions, quasi-likelihood regressions, novel non-parametric regressions. Each chapter can be studied as a stand-alone, and covers one field in the fast growing world of regression analyses. The authors, as professors in statistics and machine learning at European universities, are worried, that their students find regression-analyses harder than any other methodology in statistics. This is serious, because almost all of the novel methodologies in current data mining and data analysis include elements of regression-analysis. It is the main incentive for writing this 28 chapter edition, consistent of - 28 major fields of regression analysis, - their condensed maths, - their applications in medical and health research as published so far, - step by step analyses for self-assessment, - conclusion and reference sections. Traditional regression analysis is adequate for epidemiology, but lacks the precision required for clinical investigations. However, in the past two decades modern regression methods have proven to be much more precise. And so it is time, that a book described regression analyses for clinicians. The current edition is the first to do so. It is written for a non-mathematical readership. Self-assessment data-files are provided through Springer' s "Extras Online".

Understanding Clinical Data Analysis

Author: Ton J. Cleophas
Publisher: Springer
ISBN: 3319395866
Size: 71.43 MB
Format: PDF, Docs
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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 abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? The book will cover the WHY-SOs.

Matlab Primer For Speech Language Pathology And Audiology

Author: Frank R. Boutsen
Publisher: Plural Publishing
ISBN: 1597569496
Size: 16.50 MB
Format: PDF, Docs
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MATLAB® Primer for Speech-Language Pathology and Audiology provides training and access to MATLAB®, the computational language developed by MathWorks®. While there are MATLAB® textbooks and manuals written for the field of engineering, there are no textbooks targeting allied heath disciplines, particularly speech-language pathology and audiology. Research and practice in this field can greatly benefit from quantification and automation in data management, a domain that is increasingly labor-intensive. The text anticipates and promotes increased reliance on quantification and automation in the fields of speech-language pathology and audiology. This book is intended for students, practitioners, and researchers in speech-language pathology and audiology who wish to increase their productivity by incorporating and automating common research procedures and data-analysis calculations, or who wish to develop new tools and methods for their own paradigms and data processing. It assumes no prior knowledge of programming, but requires the reader to have a grasp of basic computer skills, such as managing folders, moving files, and navigating file paths and folder structures. Content and style are chosen so as to lower the threshold for an audience who has limited training in computer science. Concepts are presented in a personalized writing style (almost a dialogue with the reader), along with a didactic format similar to programmed instruction, using applications and work assignments that are concrete and manageable. Key features include: * A comprehensive introduction for the user in an effort to limit background knowledge needed to understand the content * Several mathematical review appendices * Exercises for the student to apply skills learned in laboratory and clinical applications Disclaimer: Please note that ancillary content (such documents, audio, and video) may not be included as published in the original print version of this book.

Machine Learning In Radiation Oncology

Author: Issam El Naqa
Publisher: Springer
ISBN: 3319183052
Size: 14.15 MB
Format: PDF, ePub, Mobi
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​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Machine Learning In Medicine

Author: Ton J. Cleophas
Publisher: Springer Science & Business Media
ISBN: 9400768869
Size: 47.12 MB
Format: PDF, Mobi
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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 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.

Machine Learning In Medical Imaging

Author: Guorong Wu
Publisher: Springer
ISBN: 3319022679
Size: 32.22 MB
Format: PDF, Kindle
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This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, in Nagoya, Japan, in September 2013. The 32 contributions included in this volume were carefully reviewed and selected from 57 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.

Machine Learning In Medicine Cookbook

Author: Ton J. Cleophas
Publisher: Springer Science & Business Media
ISBN: 3319041819
Size: 18.16 MB
Format: PDF, Kindle
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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 form the subjects reviewed in the 750 page three volume textbook by the same authors, entitled “Machine Learning in Medicine I-III” (ed. by Springer, Heidelberg, Germany, 2013) and was written as a hand-hold presentation and must-read publication. It was written not only to investigators and students in the fields, but also to jaded clinicians new to the methods and lacking time to read the entire textbooks. General purposes and scientific questions of the methods are only briefly mentioned, but full attention is given to the technical details. The two authors, a statistician and current president of the International Association of Biostatistics and a clinician and past-president of the American College of Angiology, provide plenty of step-by-step analyses from their own research and data files for self-assessment are available at extras.springer.com. From their experience the authors demonstrate that machine learning performs sometimes better than traditional statistics does. Machine learning may have little options for adjusting confounding and interaction, but you can add propensity scores and interaction variables to almost any machine learning method.