Machine learning in medicine - cookbook: 3
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham [u.a.]
Springer
2014
|
| Schriftenreihe: | SpringerBriefs in statistics
SpringerBriefs in statistics |
| Online-Zugang: | FHD01 Volltext Inhaltsverzeichnis Abstract |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9783319121628 9783319121635 |
| DOI: | 10.1007/978-3-319-12163-5 |
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Datensatz im Suchindex
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| adam_text | MACHINE LEARNING IN MEDICINE - COOKBOOK THREE
/ CLEOPHAS, TON J.
: 2014
TABLE OF CONTENTS / INHALTSVERZEICHNIS
PREFACE
I. CLUSTER MODELS
HIERARCHICAL CLUSTERING AND K-MEANS CLUSTERING TO IDENTIFY SUBGROUPS IN
SURVEYS.-DENSITY-BASED CLUSTERING TO IDENTIFY OUTLIER GROUPS IN
OTHERWISE HOMOGENEOUS DATA.-TWO STEP CLUSTERING TO IDENTIFY SUBGROUPS
AND PREDICT SUBGROUP MEMBERSHIPS
II. LINEAR MODELS.-LINEAR, LOGISTIC, AND COX REGRESSION FOR OUTCOME
PREDICTION WITH UNPAIRED DATA.-GENERALIZED LINEAR MODELS FOR OUTCOME
PREDICTION WITH PAIRED DATA.-GENERALIZED LINEAR MODELS FOR PREDICTING
EVENT-RATES.-FACTOR ANALYSIS AND PARTIAL LEAST SQUARES (PLS) FOR
COMPLEX-DATA REDUCTION
OPTIMAL SCALING OF HIGH-SENSITIVITY ANALYSIS OF HEALTH
PREDICTORS.-DISCRIMINANT ANALYSIS FOR MAKING A DIAGNOSIS FROM MULTIPLE
OUTCOMES.-WEIGHTED LEAST SQUARES FOR ADJUSTING EFFICACY DATA WITH
INCONSISTENT SPREAD.-PARTIAL CORRELATIONS FOR REMOVING INTERACTION
EFFECTS FROM EFFICACY DATA.-CANONICAL REGRESSION FOR OVERALL
STATISTICS OF MULTIVARIATE DATA
III. RULES MODELS
NEURAL NETWORKS FOR ASSESSING RELATIONSHIPS THAT ARE TYPICALLY
NONLINEAR.-COMPLEX SAMPLES METHODOLOGIES FOR UNBIASED
SAMPLING.-CORRESPONDENCE ANALYSIS FOR IDENTIFYING THE BEST OF MULTIPLE
TREATMENTS IN MULTIPLE GROUPS.-DECISION TREES FOR DECISION
ANALYSIS.-MULTIDIMENSIONAL SCALING FOR VISUALIZING EXPERIENCED DRUG
EFFICACIES.-STOCHASTIC PROCESSES FOR LONG TERM PREDICTIONS FROM SHORT
TERM OBSERVATIONS.-OPTIMAL BINNING FOR FINDING HIGH RISK
CUT-OFFS.-CONJOINT ANALYSIS FOR DETERMINING THE MOST APPRECIATED
PROPERTIES OF MEDICINES TO BE DEVELOPED
INDEX
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
MACHINE LEARNING IN MEDICINE - COOKBOOK THREE
/ CLEOPHAS, TON J.
: 2014
ABSTRACT / INHALTSTEXT
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 . NO OTHER
SELF-ASSESSMENT WORKS FOR THE MEDICAL AND HEALTH CARE COMMUNITY COVERING
THE FIELD OF MACHINE LEARNING HAVE BEEN PUBLISHED TO DATE. 2. EACH
CHAPTER OF THE BOOK CAN BE STUDIED WITHOUT THE NEED TO CONSULT OTHER
CHAPTERS, AND CAN, FOR THE READERSHIP S CONVENIENCE, BE DOWNLOADED FROM
THE INTERNET. SELF-ASSESSMENT EXAMPLES ARE AVAILABLE AT
EXTRAS.SPRINGER.COM. 3. AN ADEQUATE COMMAND OF MACHINE LEARNING
METHODOLOGIES IS A REQUIREMENT FOR PHYSICIANS AND OTHER HEALTH WORKERS,
PARTICULARLY NOW, BECAUSE THE AMOUNT OF MEDICAL COMPUTER DATA FILES
CURRENTLY DOUBLES EVERY 20 MONTHS, AND, BECAUSE, SOON, IT WILL BE
IMPOSSIBLE FOR THEM TO TAKE PROPER DATA-BASED HEALTH DECISIONS WITHOUT
THE HELP OF MACHINE LEARNING. 4.GIVEN THE IMPORTANCE OF KNOWLEDGE OF
MACHINE LEARNING IN THE MEDICAL AND HEALTH CARE COMMUNITY, AND THE
CURRENT LACK OF KNOWLEDGE OF IT, THE READERSHIP WILL CONSIST OF ANY
PHYSICIAN AND HEALTH WORKER. 5. THE BOOK WAS WRITTEN IN A SIMPLE
LANGUAGE IN ORDER TO ENHANCE READABILITYNOT ONLY FOR THE ADVANCED BUT
ALSO FOR THE NOVICES. 6. THE BOOK IS MULTIPURPOSE, IT IS AN INTRODUCTION
FOR IGNORANT, A PRIMER FOR THE INEXPERIENCED, AND A SELF-ASSESSMENT
HANDBOOK FOR THE ADVANCED. 7. THE BOOK, WAS, PARTICULARLY, WRITTEN FOR
JADED PHYSICIANS AND ANY OTHER HEALTH CARE PROFESSIONALS LACKING TIME TO
READ THE ENTIRE SERIES OF THREE TEXTBOOKS. 8. LIKE THE OTHER TWO
COOKBOOKS IT CONTAINS TECHNICAL DESCRIPTIONS AND SELF-ASSESSMENT
EXAMPLES OF 20 IMPORTANT COMPUTER METHODOLOGIES FOR MEDICAL DATA
ANALYSIS, AND IT, LARGELY, SKIPS THE THEORETICAL AND MATHEMATICAL
BACKGROUND. 9.INFORMATION OF THEORETICAL AND MATHEMATICAL BACKGROUND OF
THE METHODS DESCRIBED ARE DISPLAYED IN A NOTES SECTION AT THE END OF
EACH CHAPTER. 10.UNLIKE TRADITIONAL STATISTICAL METHODS, THE MACHINE
LEARNING METHODOLOGIES ARE ABLE TO ANALYZE BIG DATA INCLUDING THOUSANDS
OF CASES AND HUNDREDS OF VARIABLES. 11. THE MEDICAL AND HEALTH CARE
COMMUNITY IS LITTLE AWARE OF THE MULTIDIMENSIONAL NATURE OF CURRENT
MEDICAL DATA FILES, AND EXPERIMENTAL CLINICAL STUDIES ARE NOT HELPFUL TO
THAT AIM EITHER, BECAUSE THESE STUDIES, USUALLY, ASSUME THAT SUBGROUP
CHARACTERISTICS ARE UNIMPORTANT, AS LONG AS THE STUDY IS RANDOMIZED.
THIS IS, OF COURSE, UNTRUE, BECAUSE ANY SUBGROUP CHARACTERISTIC MAY BE
VITAL TO AN INDIVIDUAL AT RISK. 12.TO DATE, EXCEPT FOR A THREE VOLUME
INTRODUCTARY SERIES ON THE SUBJECT ENTITLED MACHINE LEARNING IN
MEDICINE PART ONE, TWO, AND THEE, 2013, SPRINGER HEIDELBERG GERMANY
FROM THE SAME AUTHORS, AND THE CURRENT COOKBOOK SERIES, NO BOOKS ON
MACHINE LEARNING IN MEDICINE HAVE BEEN PUBLISHED. 13. ANOTHER UNIQUE
FEATURE OF THE COOKBOOKS IS THAT IT WAS JOINTLY WRITTEN BY TWO AUTHORS
FROM DIFFERENT DISCIPLINES, ONE BEING A CLINICIAN/CLINICAL
PHARMACOLOGIST, ONE BEING A MATHEMATICIAN/BIOSTATISTICIAN. 14. THE
AUTHORS HAVE ALSO JOINTLY BEEN TEACHING AT UNIVERSITIES AND INSTITUTIONS
THROUGHOUT EUROPE AND THE USA FOR THE PAST 20 YEARS. 15. THE AUTHORS
HAVE MANAGED TO COVER THE FIELD OF MEDICAL DATA ANALYSIS IN A
NONMATHEMATICAL WAY FOR THE BENEFIT OF MEDICAL AND HEALTH WORKERS.
16.THE AUTHORS ALREADY SUCCESSFULLY PUBLISHED MANY STATISTICS TEXTBOOKS
AND SELF-ASSESSMENT BOOKS, E.G., THE 67 CHAPTER TEXTBOOK ENTITLED
STATISTICS APPLIED TO CLINICAL STUDIES 5TH EDITION, 2012, SPRINGER
HEIDELBERG GERMANY WITH DOWNLOADS OF 62,826 COPIES. 17. THE CURRENT
COOKBOOK MAKES USE, IN ADDITION TO SPSS STATISTICAL SOFTWARE, OF VARIOUS
FREE CALCULATORS FROM THE INTERNET, AS WELL AS THE KONSTANZ INFORMATION
MINER (KNIME), A WIDELY APPROVED FREE MACHINE LEARNING PACKAGE, AND THE
FREE WEKA DATA MINING PACKAGE FROM NEW ZEALAND. 18. THE ABOVE SOFTWARE
PACKAGES WITH HUNDREDS OF NODES, THE BASIC PROCESSING UNITS INCLUDING
VIRTUALLY ALL OF THE STATISTICAL AND DATA MINING METHODS, CAN BE USED
NOT ONLY FOR DATA ANALYSES, BUT ALSO FOR APPROPRIATE DATA STORAGE. 19.
THE CURRENT COOKBOOK SHOWS, PARTICULARLY, FOR THOSE WITH LITTLE AFFINITY
TO VALUE TABLES, THAT DATA MINING IN THE FORM OF A VISUALIZATION PROCESS
IS VERY WELL FEASIBLE, AND OFTEN MORE REVEALING THAN TRADITIONAL
STATISTICS.20.THE KNIME AND WEKA DATA MINERS USES WIDELY AVAILABLE EXCEL
DATA FILES. 21. IN CURRENT CLINICAL RESEARCH PROSPECTIVE COHORT STUDIES
ARE INCREASINGLY REPLACING THE COSTLY CONTROLLED CLINICAL TRIALS, AND
MODERN MACHINE LEARNING METHODOLOGIES LIKE PROBIT AND TOBIT REGRESSIONS
AS WELL AS NEURAL NETWORKS, BAYESIAN NETWORKS, AND SUPPORT VECTOR
MACHINES PROVE TO BETTER FIT THEIR ANALYSIS THAN TRADITIONAL STATISTICAL
METHODS DO. 22. THE CURRENT COOKBOOK NOT ONLY INCLUDES CONCISE
DESCRIPTIONS OF STANDARD MACHINE LEARNING METHODS, BUT ALSO OF MORE
RECENT METHODS LIKE THE LINEAR MACHINE LEARNING MODELS USING ORDINAL AND
LOGLINEAR REGRESSION. 23. MACHINE LEARNING TENDS TO INCREASINGLY USE
EVOLUTIONARY OPERATION METHODOLOGIES. ALSO THIS SUBJECT HAS BEEN
COVERED. 24. ALL OF THE METHODS DESCRIBED HAVE BEEN APPLIED IN THE
AUTHORS OWN RESEARCH PRIOR TO THIS PUBLICATION
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
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| spelling | Cleophas, Ton J. 1947- Verfasser (DE-588)1069424161 aut Machine learning in medicine - cookbook 3 Ton J. Cleophas ; Aeilko H. Zwinderman Cham [u.a.] Springer 2014 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier SpringerBriefs in statistics Zwinderman, Aeilko H. 1960- Verfasser (DE-588)108382225X aut (DE-604)BV041927763 3 https://doi.org/10.1007/978-3-319-12163-5 Verlag Volltext Springer Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027645013&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Springer Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027645013&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Abstract |
| spellingShingle | Cleophas, Ton J. 1947- Zwinderman, Aeilko H. 1960- Machine learning in medicine - cookbook |
| title | Machine learning in medicine - cookbook |
| title_auth | Machine learning in medicine - cookbook |
| title_exact_search | Machine learning in medicine - cookbook |
| title_full | Machine learning in medicine - cookbook 3 Ton J. Cleophas ; Aeilko H. Zwinderman |
| title_fullStr | Machine learning in medicine - cookbook 3 Ton J. Cleophas ; Aeilko H. Zwinderman |
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| title_short | Machine learning in medicine - cookbook |
| title_sort | machine learning in medicine cookbook |
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