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

Автор: Kevin Murphy
Название: Machine Learning
ISBN: 0262018020 ISBN-13(EAN): 9780262018029
Издательство: MIT Press
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Цена: 124150.00 T
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Описание:

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Автор: Darren Cook
Название: Practical Machine Learning with H2O
ISBN: 149196460X ISBN-13(EAN): 9781491964606
Издательство: Wiley
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Цена: 42230.00 T
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Описание: This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.

Автор: Malley
Название: Statistical Learning for Biomedical Data
ISBN: 0521699096 ISBN-13(EAN): 9780521699099
Издательство: Cambridge Academ
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Цена: 43290.00 T
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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 - Cookbook Two
ISBN: 3319074121 ISBN-13(EAN): 9783319074122
Издательство: Springer
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Цена: 46570.00 T
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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: Cleophas
Название: Machine Learning in Medicine - Cookbook
ISBN: 3319041800 ISBN-13(EAN): 9783319041803
Издательство: Springer
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Цена: 46570.00 T
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Описание: This timely guide explains how to derive the most benefit from automatically mining the vast, and growing, quantities of information in medical databases. It lays out the fruits of the authors` expertise in a condensed, easily consulted format.

Автор: Cleophas
Название: Machine Learning in Medicine
ISBN: 9400778686 ISBN-13(EAN): 9789400778689
Издательство: Springer
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Цена: 83850.00 T
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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: Ton J. Cleophas; Aeilko H. Zwinderman
Название: Machine Learning in Medicine - Cookbook Three
ISBN: 3319121626 ISBN-13(EAN): 9783319121628
Издательство: Springer
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Цена: 56590.00 T
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Описание: 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".

Автор: Cleophas Ton J
Название: Machine Learning in Medicine
ISBN: 9400758235 ISBN-13(EAN): 9789400758230
Издательство: Springer
Цена: 83850.00 T
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Описание:

Preface.- 1 Introduction to machine learning.- 2 Logistic regression for health profiling.- 3 Optimal scaling: discretization.- 4 Optimal scaling: regularization including ridge, lasso, and elastic net regression.- 5 Partial correlations.- 6 Mixed linear modelling.- 7 Binary partitioning.- 8 Item response modelling.- 9 Time-dependent predictor modelling.- 10 Seasonality assessments.- 11 Non-linear modelling.- 12 Artificial intelligence, multilayer Perceptron modelling.- 13 Artificial intelligence, radial basis function modelling.- 14 Factor analysis.- 15 Hierarchical cluster analysis for unsupervised data.- 16 Partial least squares.- 17 Discriminant analysis for Supervised data.- 18 Canonical regression.- 19 Fuzzy modelling.- 20 Conclusions. Index.

Автор: Giraud
Название: Introduction to High-Dimensional Statistics
ISBN: 1482237946 ISBN-13(EAN): 9781482237948
Издательство: Taylor&Francis
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Цена: 64300.00 T
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Описание: Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-study.

Автор: Mitchell
Название: Machine Learning
ISBN: 0071154671 ISBN-13(EAN): 9780071154673
Издательство: McGraw-Hill
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Цена: 69770.00 T
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Описание: Covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

Автор: Steptoe, Andrew
Название: Handbook of Behavioral Medicine
ISBN: 1441964835 ISBN-13(EAN): 9781441964830
Издательство: Springer
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Цена: 167700.00 T
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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: 73920.00 T
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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.