Machine Learning in Medicine, Ton J. Cleophas; Aeilko H. Zwinderman

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Автор: Christopher M. Bishop
Название: Pattern Recognition and Machine Learning
ISBN: 0387310738 ISBN-13(EAN): 9780387310732
Издательство: Springer
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Цена: 11878.00 р.
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Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Автор: Steptoe, Andrew
Название: Handbook of Behavioral Medicine
ISBN: 1441964835 ISBN-13(EAN): 9781441964830
Издательство: Springer
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Цена: 25155.00 р.
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Описание: This handbook provides an up-to-date survey of methods and applications across the broad range of behavioral medicine research and practice. It is based on cutting-edge methodologies and applications that are relevant across different medical conditions.

Автор: Barber
Название: Bayesian Reasoning and Machine Learning
ISBN: 0521518148 ISBN-13(EAN): 9780521518147
Издательство: Cambridge Academ
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Цена: 11088.00 р.
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Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.

Автор: Ton J. Cleophas; Aeilko H. Zwinderman
Название: Machine Learning in Medicine
ISBN: 9400795122 ISBN-13(EAN): 9789400795129
Издательство: Springer
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Цена: 9794.00 р.
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Описание: This second of a two-volume work includes coverage of various clustering models, support vector machines, Bayesian networks, discrete wavelet analysis, genetic programming, association rule learning, anomaly detection and correspondence analysis.

Автор: Ratner Bruce
Название: Statistical and Machine-Learning Data Mining
ISBN: 1439860912 ISBN-13(EAN): 9781439860915
Издательство: Taylor&Francis
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Цена: 9033.00 р.
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Описание: Rev. ed. of: Statistical modeling and analysis for database marketing. c2003.

Автор: Hastie
Название: Statistical Learning with Sparsity
ISBN: 1498712169 ISBN-13(EAN): 9781498712163
Издательство: Taylor&Francis
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Цена: 16843.00 р.
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Описание:

Discover New Methods for Dealing with High-Dimensional Data

A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.

Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso.

In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

Автор: Sharon E. Straus
Название: Evidence-Based Medicine, 4th Edition
ISBN: 0702031275 ISBN-13(EAN): 9780702031274
Издательство: Elsevier Science
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Цена: 6483.00 р.
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Описание: Provides an explanation of the central questions: how to ask answerable clinical questions; how to translate them into effective searches for the best evidence; how to critically appraise that evidence for its validity and importance; and, how to integrate it with patients` values and preferences.

Автор: Malley
Название: Statistical Learning for Biomedical Data
ISBN: 0521699096 ISBN-13(EAN): 9780521699099
Издательство: Cambridge Academ
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Цена: 6494.00 р.
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Описание: Biomedical researchers need machine learning techniques to make predictions such as survival/death or response to treatment when data sets are large and complex. This highly motivating introduction to these machines explains underlying principles in nontechnical language, using many examples and figures, and connects these new methods to familiar techniques.

Автор: Cleophas
Название: Machine Learning in Medicine
ISBN: 9400778686 ISBN-13(EAN): 9789400778689
Издательство: Springer
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Цена: 12577.00 р.
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Описание: 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.

Автор: Cleophas
Название: Machine Learning in Medicine - Cookbook Two
ISBN: 3319074121 ISBN-13(EAN): 9783319074122
Издательство: Springer
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Цена: 6986.00 р.
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Описание:

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 incentive for the authors to complete a series of three textbooks entitled "Machine Learning in Medicine Part One, Two and Three, Springer Heidelberg Germany, 2012-2013," describing in a nonmathematical way over sixty machine learning methodologies, as available in SPSS statistical software and other major software programs. Although well received, it came to our attention that physicians and students often lacked time to read the entire books, and requested a small book, without background information and theoretical discussions and highlighting technical details.

For this reason we produced a 100 page cookbook, entitled "Machine Learning in Medicine - Cookbook One," with data examples available at extras.springer.com for self-assessment and with reference to the above textbooks for background information. Already at the completion of this cookbook we came to realize, that many essential methods were not covered. The current volume, entitled "Machine Learning in Medicine - Cookbook Two" is complementary to the first and also intended for providing a more balanced view of the field and thus, 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.

Similarly to Machine Learning in Medicine - Cookbook One, the current work will describe stepwise analyses of over twenty machine learning methods, that are, likewise, based on the three major machine learning methodologies:

• Cluster methodologies (Chaps. 1-3)
  
• Linear methodologies (Chaps. 4-11)
  
• Rules methodologies (Chaps. 12-20)

In extras.springer.com the data files of the examples are given, as well as XML (Extended Mark up Language), SPS (Syntax) and ZIP (compressed) files for outcome predictions in future patients. In addition to condensed versions of the methods, fully described in the above three textbooks, an introduction is given to SPSS Modeler (SPSS' data mining workbench) in the Chaps. 15, 18, 19, while improved statistical methods like various automated analyses and Monte Carlo simulation models are in the Chaps. 1, 5, 7 and 8.

We should emphasize that all of the methods described have been successfully applied in practice by the authors, both of them professors in applied statistics and machine learning at the European Community College of Pharmaceutical Medicine in Lyon France. We recommend the current work not only as a training companion to investigators and students, because of plenty of step by step analyses given, but also as a brief introductory text to jaded clinicians new to the methods. For the latter purpose, background and theoretical information have been replaced with the appropriate references to the above textbooks, while single sections addressing "general purposes," "main scientific questions" and "conclusions" are given in place.

Finally, we will demonstrate that modern 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.

Автор: Ton J. Cleophas; Aeilko H. Zwinderman
Название: Machine Learning in Medicine
ISBN: 9402402608 ISBN-13(EAN): 9789402402605
Издательство: Springer
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Цена: 9794.00 р.
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Описание: 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.