The Resource Machine Learning in Medicine - Cookbook Two, by Ton J. Cleophas, Aeilko H. Zwinderman, (electronic resource)

Machine Learning in Medicine - Cookbook Two, by Ton J. Cleophas, Aeilko H. Zwinderman, (electronic resource)

Label
Machine Learning in Medicine - Cookbook Two
Title
Machine Learning in Medicine - Cookbook Two
Statement of responsibility
by Ton J. Cleophas, Aeilko H. Zwinderman
Creator
Contributor
Author
Provider
Subject
Language
eng
Summary
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
Member of
http://library.link/vocab/creatorName
Cleophas, Ton J
Image bit depth
0
LC call number
R1
Literary form
non fiction
http://library.link/vocab/relatedWorkOrContributorName
  • Zwinderman, Aeilko H.
  • SpringerLink
Series statement
SpringerBriefs in Statistics,
Series volume
49
http://library.link/vocab/subjectName
  • Medicine
  • Biometrics
  • Computer science
  • Statistical methods
  • Statistics
  • Medicine & Public Health
  • Medicine/Public Health, general
  • Biostatistics
  • Statistics for Life Sciences, Medicine, Health Sciences
  • Computer Applications
  • Biometrics
Label
Machine Learning in Medicine - Cookbook Two, by Ton J. Cleophas, Aeilko H. Zwinderman, (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Preface. I Cluster models -- Nearest Neighbors for Classifying New Medicines -- Predicting High-Risk-Bin Memberships -- Predicting Outlier Memberships -- Linear Models -- Polynomial Regression for Outcome Categories -- Automatic Nonparametric Tests for Predictor Categories- Random Intercept Models for Both Outcome and Predictor -- Automatic Regression for Maximizing Linear Relationships -- Simulation Models for Varying Predictors -- Generalized Linear Mixed Models for Outcome Prediction from Mixed Data -- Two Stage Least Squares for Linear Models with Problematic -- Autoregressive Models for Longitudinal Data. II Rules Models -- Item Response Modeling for Analyzing Quality of Life with Better Precision -- Survival Studies with Varying Risks of Dying -- Fuzzy Logic for Improved Precision of Pharmacological Data Analysis -- Automatic Data Mining for the Best Treatment of a Disease -- Pareto Charts for Identifying the Main Factors of Multifactorial -- Radial Basis Neural Networks for Multidimensional Gaussian -- Automatic Modeling for Drug Efficacy Prediction -- Automatic Modeling for Clinical Event Prediction -- Automatic Newton Modeling in Clinical Pharmacology -- Index
Dimensions
unknown
Extent
XI, 140 p. 49 illus.
File format
multiple file formats
Form of item
electronic
Isbn
9783319074139
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-07413-9
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-319-07413-9
Label
Machine Learning in Medicine - Cookbook Two, by Ton J. Cleophas, Aeilko H. Zwinderman, (electronic resource)
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Preface. I Cluster models -- Nearest Neighbors for Classifying New Medicines -- Predicting High-Risk-Bin Memberships -- Predicting Outlier Memberships -- Linear Models -- Polynomial Regression for Outcome Categories -- Automatic Nonparametric Tests for Predictor Categories- Random Intercept Models for Both Outcome and Predictor -- Automatic Regression for Maximizing Linear Relationships -- Simulation Models for Varying Predictors -- Generalized Linear Mixed Models for Outcome Prediction from Mixed Data -- Two Stage Least Squares for Linear Models with Problematic -- Autoregressive Models for Longitudinal Data. II Rules Models -- Item Response Modeling for Analyzing Quality of Life with Better Precision -- Survival Studies with Varying Risks of Dying -- Fuzzy Logic for Improved Precision of Pharmacological Data Analysis -- Automatic Data Mining for the Best Treatment of a Disease -- Pareto Charts for Identifying the Main Factors of Multifactorial -- Radial Basis Neural Networks for Multidimensional Gaussian -- Automatic Modeling for Drug Efficacy Prediction -- Automatic Modeling for Clinical Event Prediction -- Automatic Newton Modeling in Clinical Pharmacology -- Index
Dimensions
unknown
Extent
XI, 140 p. 49 illus.
File format
multiple file formats
Form of item
electronic
Isbn
9783319074139
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-07413-9
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-319-07413-9

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