Machine Learning in Medicine - Cookbook

Nonfiction, Health & Well Being, Medical, Reference, Biostatistics, Science & Nature, Science, Biological Sciences
Cover of the book Machine Learning in Medicine - Cookbook by Ton J. Cleophas, Aeilko H. Zwinderman, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Ton J. Cleophas, Aeilko H. Zwinderman ISBN: 9783319041810
Publisher: Springer International Publishing Publication: January 3, 2014
Imprint: Springer Language: English
Author: Ton J. Cleophas, Aeilko H. Zwinderman
ISBN: 9783319041810
Publisher: Springer International Publishing
Publication: January 3, 2014
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Sedimentation Processes in the White Sea by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Recent Advances in Information Systems and Technologies by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Towards Integrative Machine Learning and Knowledge Extraction by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Bioengineering Applications of Carbon Nanostructures by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book HCI International 2015 - Posters’ Extended Abstracts by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Pedestrian and Evacuation Dynamics 2012 by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Computer Vision – ACCV 2016 by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Leptin by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Modelling Pulsar Wind Nebulae by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Incentives and Performance by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Application and Theory of Petri Nets and Concurrency by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Tutorials in Patellofemoral Disorders by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Hermitian Analysis by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Diagnosability, Security and Safety of Hybrid Dynamic and Cyber-Physical Systems by Ton J. Cleophas, Aeilko H. Zwinderman
Cover of the book Flora and Vegetation of the Czech Republic by Ton J. Cleophas, Aeilko H. Zwinderman
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy