Machine Learning in Medicine - a Complete Overview:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham [u.a.]
Springer
2015
|
| Subjects: | |
| Online Access: | TUM01 UBT01 Volltext Inhaltsverzeichnis Abstract |
| Physical Description: | 1 Online-Ressource (XXIV, 516 p. 159 illus) |
| ISBN: | 9783319151953 |
| DOI: | 10.1007/978-3-319-15195-3 |
Staff View
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Record in the Search Index
| _version_ | 1804153211436662784 |
|---|---|
| adam_text | MACHINE LEARNING IN MEDICINE - A COMPLETE OVERVIEW
/ CLEOPHAS, TON J.
: 2015
TABLE OF CONTENTS / INHALTSVERZEICHNIS
PREFACE. SECTION I CLUSTER AND CLASSIFICATION MODELS
HIERARCHICAL CLUSTERING AND K-MEANS CLUSTERING TO IDENTIFYSUBGROUPS IN
SURVEYS (50 PATIENTS)
DENSITY-BASED CLUSTERING TO IDENTIFY OUTLIER GROUPS IN OTHERWISE
HOMOGENEOUS DATA (50 PATIENTS)
TWO STEP CLUSTERING TO IDENTIFY SUBGROUPS AND PREDICT SUBGROUP
MEMBERSHIPS IN INDIVIDUAL FUTURE PATIENTS (120 PATIENTS)- NEAREST
NEIGHBORS FOR CLASSIFYING NEW MEDICINES (2 NEW AND 25 OLD OPIOIDS)-
PREDICTING HIGH-RISK-BIN MEMBERSHIPS (1445 FAMILIES)
PREDICTING OUTLIER MEMBERSHIPS (2000 PATIENTS)
DATA MINING FOR VISUALIZATION OF HEALTH PROCESSES (150 PATIENTS)
8TRAINED DECISION TREES FOR A MORE MEANINGFUL ACCURACY (150 PATIENTS)
TYPOLOGY OF MEDICAL DATA (51 PATIENTS)
PREDICTIONS FROM NOMINAL CLINICAL DATA (450 PATIENTS)
PREDICTIONS FROM ORDINAL CLINICAL DATA (450 PATIENTS)
ASSESSING RELATIVE HEALTH RISKS (3000 SUBJECTS)
MEASUREMENT AGREEMENTS (30 PATIENTS)
COLUMN PROPORTIONS FOR TESTING DIFFERENCES BETWEEN OUTCOME SCORES (450
PATIENTS)
PIVOTING TRAYS AND TABLES FOR IMPROVED ANALYSIS OF MULTIDIMENSIONAL DATA
(450 PATIENTS)
ONLINE ANALYTICAL PROCEDURE CUBES FOR A MORE RAPID APPROACH
TOANALYZING FREQUENCIES (450 PATIENTS)
RESTRUCTURE DATA WIZARD FOR DATA CLASSIFIED THE WRONG WAY (20
PATIENTS).-CONTROL CHARTS FOR QUALITY CONTROL OF MEDICINES (164 TABLET
DESINTEGRATION TIMES)
SECTION II (LOG) LINEAR MODELS
LINEAR, LOGISTIC, AND COX REGRESSION FOR OUTCOME PREDICTION WITH
UNPAIRED DATA (20, 55, AND 60 PATIENTS).-GENERALIZED LINEAR MODELS FOR
OUTCOME PREDICTION WITH PAIRED DATA (100 PATIENTS AND 139 PHYSICIANS)
GENERALIZED LINEAR MODELS FOR PREDICTING EVENT-RATES (50
PATIENTS).-FACTOR ANALYSIS AND PARTIAL LEAST SQUARES (PLS) FOR
COMPLEX-DATA REDUCTION (250 PATIENTS)
OPTIMAL SCALING OF HIGH-SENSITIVITY ANALYSIS OF HEALTH PREDICTORS (250
PATIENTS)
DISCRIMINANT ANALYSIS FOR MAKING A DIAGNOSIS FROM MULTIPLE OUTCOMES (45
PATIENTS)
WEIGHTED LEAST SQUARES FOR ADJUSTING EFFICACY DATA WITHINCONSISTENT
SPREAD (78 PATIENTS)
PARTIAL CORRELATIONS FOR REMOVING INTERACTION EFFECTS FROM EFFICACY DATA
(64 PATIENTS)
CANONICAL REGRESSION FOR OVERALL STATISTICS OF MULTIVARIATE DATA (250
PATIENTS)
MULTINOMIAL REGRESSION FOR OUTCOME CATEGORIES (55 PATIENTS)
VARIOUS METHODS FOR ANALYZING PREDICTOR CATEGORIES (60 AND 30 PATIENTS)
RANDOM INTERCEPT MODELS FOR BOTH OUTCOME AND PREDICTOR CATEGORIES (55
PATIENTS).-AUTOMATIC REGRESSION FOR MAXIMIZING LINEAR RELATIONSHIPS
(55 PATIENTS)
SIMULATION MODELS FOR VARYING PREDICTORS (9000 PATIENTS)
GENERALIZED LINEAR MIXED MODELS FOR OUTCOME PREDICTION FROM MIXED DATA
(20 PATIENTS)
TWO STAGE LEAST SQUARES FOR LINEAR MODELS WITH PROBLEMATIC PREDICTORS
(35 PATIENTS)
AUTOREGRESSIVE MODELS FOR LONGITUDINAL DATA (120 MONTHLY POPULATION
RECORDS)
VARIANCE COMPONENTS FOR ASSESSING THE MAGNITUDE OF RANDOM EFFECTS (40
PATIENTS)
ORDINAL SCALING FOR CLINICAL SCORES WITH INCONSISTENT INTERVALS (900
PATIENTS)
LOGLINEAR MODELS FOR ASSESSING INCIDENT RATES WITH VARYING INCIDENT
RISKS (12 POPULATIONS).-LOGLINEAR MODELS FOR OUTCOME CATEGORIES (445
PATIENTS)
HETEROGENEITY IN CLINICAL RESEARCH: MECHANISMS RESPONSIBLEM (20 STUDIES)
PERFORMANCE EVALUATION OF NOVEL DIAGNOSTIC TESTS (650 AND 588
PATIENTS).-QUANTILE - QUANTILE PLOTS, A GOOD START FOR LOOKING AT YOUR
MEDICAL DATA (50 CHOLESTEROL MEASUREMENTS AND 52 PATIENTS)
RATE ANALYSIS OF MEDICAL DATA BETTER THAN RISK ANALYSIS (52 PATIENTS)
TREND TESTS WILL BE STATISTICALLY SIGNIFICANT IF TRADITIONAL TESTS ARE
NOT (30 AND 106 PATIENTS)
DOUBLY MULTIVARIATE ANALYSIS OF VARIANCE FOR MULTIPLE OBSERVATIONS FROM
MULTIPLE OUTCOME VARIABLES (16 PATIENTS)
PROBIT MODELS FOR ESTIMATING EFFECTIVE PHARMACOLOGICAL TREATMENT DOSAGES
(14 TESTS)
INTERVAL CENSORED DATA ANALYSIS FOR ASSESSING MEAN TIME TO CANCER
RELAPSE (51 PATIENTS).-STRUCTURAL EQUATION MODELING WITH SPSS ANALYSIS
OF MOMENT STRUCTURES (AMOS) FOR CAUSE EFFECT RELATIONSHIPS I (35
PATIENTS)
STRUCTURAL EQUATION MODELING WITH SPSS ANALYSIS OF MOMENT STRUCTURES
(AMOS) FOR CAUSE EFFECT RELATIONSHIPS II (35 PATIENTS)
SECTION III RULES MODELS
NEURAL NETWORKS FOR ASSESSING RELATIONSHIPS THAT ARE TYPICALLY NONLINEAR
(90 PATIENTS). COMPLEX SAMPLES METHODOLOGIES FOR UNBIASED SAMPLING
(9,678 PERSONS)
CORRESPONDENCE ANALYSIS FOR IDENTIFYING THE BEST OF MULTIPLE TREATMENTS
IN MULTIPLE GROUPS (217 PATIENTS)
DECISION TREES FOR DECISION ANALYSIS (1004 AND 953
PATIENTS).-MULTIDIMENSIONAL SCALING FOR VISUALIZING EXPERIENCED DRUG
EFFICACIES (14 PAIN-KILLERS AND 42 PATIENTS)
STOCHASTIC PROCESSES FOR LONG TERM PREDICTIONS FROM SHORT TERM
OBSERVATIONS
OPTIMAL BINNING FOR FINDING HIGH RISK CUT-OFFS (1445
FAMILIES).-CONJOINT ANALYSIS FOR DETERMINING THE MOST APPRECIATED
PROPERTIES OF MEDICINES TO BE DEVELOPED (15 PHYSICIANS)
ITEM RESPONSE MODELING FOR ANALYZING QUALITY OF LIFE WITH BETTER
PRECISION (1000 PATIENTS)
SURVIVAL STUDIES WITH VARYING RISKS OF DYING (50 AND 60 PATIENTS)
FUZZY LOGIC FOR IMPROVED PRECISION OF PHARMACOLOGICAL DATA ANALYSIS (9
INDUCTION DOSAGES)
AUTOMATIC DATA MINING FOR THE BEST TREATMENT OF A DISEASE (90 PATIENTS)
PARETO CHARTS FOR IDENTIFYING THE MAIN FACTORS OF MULTIFACTORIAL
OUTCOMES (2000 ADMISSIONS TO HOSPITAL)
RADIAL BASIS NEURAL NETWORKS FOR MULTIDIMENSIONAL GAUSSIAN DATA (90
PERSONS)
AUTOMATIC MODELING FOR DRUG EFFICACY PREDICTION (250 PATIENTS)
AUTOMATIC MODELING FOR CLINICAL EVENT PREDICTION (200 PATIENTS)
AUTOMATIC NEWTON MODELING IN CLINICAL PHARMACOLOGY (15 ALFENTANIL
DOSAGES, 15 QUINIDINE TIME-CONCENTRATION RELATIONSHIPS)
SPECTRAL PLOTS FOR HIGH SENSITIVITY ASSESSMENT OF PERIODICITY (6
YEARS’ MONTHLY C REACTIVE PROTEIN LEVELS)
RUNS TEST FOR IDENTIFYING BEST ANALYSIS MODELS (21 ESTIMATES OF QUANTITY
AND QUALITY OF PATIENT CARE)
EVOLUTIONARY OPERATIONS FOR HEALTH PROCESS IMPROVEMENT (8 OPERATION ROOM
SETTINGS).-BAYESIAN NETWORKS FOR CAUSE EFFECT MODELING (600 PATIENTS)
SUPPORT VECTOR MACHINES FOR IMPERFECT NONLINEAR DATA
MULTIPLE RESPONSE SETS FOR VISUALIZING CLINICAL DATA TRENDS (811
PATIENT VISITS)
PROTEIN AND DNA SEQUENCE MINING
ITERATION METHODS FOR CROSSVALIDATION (150 PATIENTS)
TESTING PARALLEL-GROUPS WITH DIFFERENT SAMPLE SIZES AND VARIANCES (5
PARALLEL-GROUP STUDIES)
ASSOCIATION RULES BETWEEN EXPOSURE AND OUTCOME (50 AND 60 PATIENTS)
CONFIDENCE INTERVALS FOR PROPORTIONS AND DIFFERENCES INPROPORTIONS
(100 AND 75 PATIENTS)
RATIO STATISTICS FOR EFFICACY ANALYSIS OF NEW DRUGS 50
PATIENTS).-FIFTH ORDER POLYNOMES OF CIRCADIAN RHYTHMS (1 PATIENT)
GAMMA DISTRIBUTION FOR ESTIMATING THE PREDICTORS OF MEDICAL OUTCOMES
(110 PATIENTS). INDEX
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
MACHINE LEARNING IN MEDICINE - A COMPLETE OVERVIEW
/ CLEOPHAS, TON J.
: 2015
ABSTRACT / INHALTSTEXT
THE CURRENT BOOK IS THE FIRST PUBLICATION OF A COMPLETE OVERVIEW OF
MACHINE LEARNING METHODOLOGIES FOR THE MEDICAL AND HEALTH SECTOR. IT WAS
WRITTEN AS A TRAINING COMPANION, AND 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. IN EIGHTY CHAPTERS EIGHTY
DIFFERENT MACHINE LEARNING METHODOLOGIES ARE REVIEWED, IN COMBINATION
WITH DATA EXAMPLES FOR SELF-ASSESSMENT. EACH CHAPTER CAN BE STUDIED
WITHOUT THE NEED TO CONSULT OTHER CHAPTERS. THE AMOUNT OF DATA STORED IN
THE WORLD S DATABASES DOUBLES EVERY 20 MONTHS, AND CLINICIANS, FAMILIAR
WITH TRADITIONAL STATISTICAL METHODS, ARE AT A LOSS TO ANALYZE THEM.
TRADITIONAL METHODS HAVE, INDEED, DIFFICULTY TO IDENTIFY OUTLIERS IN
LARGE DATASETS, AND TO FIND PATTERNS IN BIG DATA AND DATA WITH MULTIPLE
EXPOSURE / OUTCOME VARIABLES. IN ADDITION, ANALYSIS-RULES FOR SURVEYS
AND QUESTIONNAIRES, WHICH ARE CURRENTLY COMMON METHODS OF DATA
COLLECTION, ARE, ESSENTIALLY, MISSING. FORTUNATELY, THE NEW DISCIPLINE,
MACHINE LEARNING, IS ABLE TO COVER ALL OF THESE LIMITATIONS. SO FAR
MEDICAL PROFESSIONALS HAVE BEEN RATHER RELUCTANT TO USE MACHINE
LEARNING. ALSO, IN THE FIELD OF DIAGNOSIS MAKING, FEW DOCTORS MAY WANT A
COMPUTER CHECKING THEM, ARE INTERESTED IN COLLABORATION WITH A COMPUTER
OR WITH COMPUTER ENGINEERS. ADEQUATE HEALTH AND HEALTH CARE WILL,
HOWEVER, SOON BE IMPOSSIBLE WITHOUT PROPER DATA SUPERVISION FROM MODERN
MACHINE LEARNING METHODOLOGIES LIKE CLUSTER MODELS, NEURAL NETWORKS, AND
OTHER DATA MINING METHODOLOGIES. EACH CHAPTER STARTS WITH PURPOSES AND
SCIENTIFIC QUESTIONS. THEN, STEP-BY-STEP ANALYSES, USING DATA EXAMPLES,
ARE GIVEN. FINALLY, A PARAGRAPH WITH CONCLUSION, AND REFERENCES TO THE
CORRESPONDING SITES OF THREE INTRODUCTORY TEXTBOOKS, PREVIOUSLY WRITTEN
BY THE SAME AUTHORS, IS GIVEN
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
| any_adam_object | 1 |
| author | Cleophas, Ton J. 1947- Zwinderman, Aeilko H. 1960- |
| author_GND | (DE-588)1069424161 (DE-588)108382225X |
| author_facet | Cleophas, Ton J. 1947- Zwinderman, Aeilko H. 1960- |
| author_role | aut aut |
| author_sort | Cleophas, Ton J. 1947- |
| author_variant | t j c tj tjc a h z ah ahz |
| building | Verbundindex |
| bvnumber | BV042481820 |
| classification_rvk | ST 300 |
| classification_tum | BIO 000 |
| collection | ZDB-2-SBL |
| ctrlnum | (OCoLC)906210877 (DE-599)BVBBV042481820 |
| dewey-full | 610 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 610 - Medicine and health |
| dewey-raw | 610 |
| dewey-search | 610 |
| dewey-sort | 3610 |
| dewey-tens | 610 - Medicine and health |
| discipline | Biologie Informatik Medizin |
| doi_str_mv | 10.1007/978-3-319-15195-3 |
| format | Electronic eBook |
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| id | DE-604.BV042481820 |
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| indexdate | 2024-07-10T01:23:00Z |
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| owner | DE-91 DE-BY-TUM DE-703 |
| owner_facet | DE-91 DE-BY-TUM DE-703 |
| physical | 1 Online-Ressource (XXIV, 516 p. 159 illus) |
| psigel | ZDB-2-SBL ZDB-2-SBL_2015 |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Springer |
| record_format | marc |
| spelling | Cleophas, Ton J. 1947- Verfasser (DE-588)1069424161 aut Machine Learning in Medicine - a Complete Overview Ton J. Cleophas, Aeilko H. Zwinderman Cham [u.a.] Springer 2015 1 Online-Ressource (XXIV, 516 p. 159 illus) txt rdacontent c rdamedia cr rdacarrier Medicine Science (General) Statistics Biomedicine Biomedicine general Medicine/Public Health, general Statistics, general Science, general Medizin Naturwissenschaft Statistik Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Medizin (DE-588)4038243-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Medizin (DE-588)4038243-6 s 1\p DE-604 Zwinderman, Aeilko H. 1960- Verfasser (DE-588)108382225X aut Erscheint auch als Druckausgabe 978-3-319-15194-6 https://doi.org/10.1007/978-3-319-15195-3 Verlag Volltext Springer Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027916779&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=027916779&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Abstract 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Cleophas, Ton J. 1947- Zwinderman, Aeilko H. 1960- Machine Learning in Medicine - a Complete Overview Medicine Science (General) Statistics Biomedicine Biomedicine general Medicine/Public Health, general Statistics, general Science, general Medizin Naturwissenschaft Statistik Maschinelles Lernen (DE-588)4193754-5 gnd Medizin (DE-588)4038243-6 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4038243-6 |
| title | Machine Learning in Medicine - a Complete Overview |
| title_auth | Machine Learning in Medicine - a Complete Overview |
| title_exact_search | Machine Learning in Medicine - a Complete Overview |
| title_full | Machine Learning in Medicine - a Complete Overview Ton J. Cleophas, Aeilko H. Zwinderman |
| title_fullStr | Machine Learning in Medicine - a Complete Overview Ton J. Cleophas, Aeilko H. Zwinderman |
| title_full_unstemmed | Machine Learning in Medicine - a Complete Overview Ton J. Cleophas, Aeilko H. Zwinderman |
| title_short | Machine Learning in Medicine - a Complete Overview |
| title_sort | machine learning in medicine a complete overview |
| topic | Medicine Science (General) Statistics Biomedicine Biomedicine general Medicine/Public Health, general Statistics, general Science, general Medizin Naturwissenschaft Statistik Maschinelles Lernen (DE-588)4193754-5 gnd Medizin (DE-588)4038243-6 gnd |
| topic_facet | Medicine Science (General) Statistics Biomedicine Biomedicine general Medicine/Public Health, general Statistics, general Science, general Medizin Naturwissenschaft Statistik Maschinelles Lernen |
| url | https://doi.org/10.1007/978-3-319-15195-3 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027916779&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027916779&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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