Machine Learning in Medicine - Cookbook Two (Record no. 371442)

MARC details
000 -LEADER
fixed length control field 06706nam a22005295i 4500
001 - CONTROL NUMBER
control field 978-3-319-07413-9
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20180115171615.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 140527s2014 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783319074139
-- 978-3-319-07413-9
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-319-07413-9
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number R1
072 #7 - SUBJECT CATEGORY CODE
Subject category code MB
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code MED000000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 610
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Cleophas, Ton J.
Relator term author.
245 10 - TITLE STATEMENT
Title Machine Learning in Medicine - Cookbook Two
Medium [electronic resource] /
Statement of responsibility, etc. by Ton J. Cleophas, Aeilko H. Zwinderman.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cham :
Name of producer, publisher, distributor, manufacturer Springer International Publishing :
-- Imprint: Springer,
Date of production, publication, distribution, manufacture, or copyright notice 2014.
300 ## - PHYSICAL DESCRIPTION
Extent XI, 140 p. 49 illus.
Other physical details online resource.
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
347 ## - DIGITAL FILE CHARACTERISTICS
File type text file
Encoding format PDF
Source rda
490 1# - SERIES STATEMENT
Series statement SpringerBriefs in Statistics,
International Standard Serial Number 2191-544X ;
Volume/sequential designation 49
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Medicine.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Biometrics (Biology).
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Application software.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Biostatistics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Medicine & Public Health.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Medicine/Public Health, general.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Biostatistics.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics for Life Sciences, Medicine, Health Sciences.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Applications.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Biometrics.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Zwinderman, Aeilko H.
Relator term author.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783319074122
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title SpringerBriefs in Statistics,
International Standard Serial Number 2191-544X ;
Volume number/sequential designation 49
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://dx.doi.org/10.1007/978-3-319-07413-9">http://dx.doi.org/10.1007/978-3-319-07413-9</a>
912 ## -
-- ZDB-2-SMA
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Home library Current library Date acquired Total Checkouts Date last seen Price effective from Koha item type
  Not Lost     EBook e-Library e-Library 15/01/2018   15/01/2018 15/01/2018 eBook

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