Machine Learning in Medicine Cookbook

The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them.

Machine Learning in Medicine   Cookbook

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.

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Machine Learning in Medicine - Cookbook
Language: en
Pages: 137
Authors: Ton J. Cleophas, Aeilko H Zwinderman
Categories: Medical
Type: BOOK - Published: 2014-01-14 - Publisher: Springer

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,
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Language: en
Pages: 312
Authors: Ayman El-Baz, Jasjit S. Suri
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Machine Learning in Medicine -- a Complete Overview
Language: en
Pages: 644
Authors: Ton J. M. Cleophas
Categories: Artificial intelligence
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Adequate health and health care is no longer possible without proper data supervision from modern machine learning methodologies like cluster models, neural networks, and other data mining methodologies. The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector, and
Machine Learning in Medicine - a Complete Overview
Language: en
Pages:
Authors: Ton J. Cleophas, Aeilko H. Zwinderman
Categories: Artificial intelligence
Type: BOOK - Published: 2015 - Publisher:

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
Machine Learning in Medicine
Language: en
Pages: 265
Authors: Ton J. Cleophas, Aeilko H. Zwinderman
Categories: Medical
Type: BOOK - Published: 2013-02-12 - Publisher: Springer Science & Business Media

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