Data driven approaches for healthcare : (Record no. 126394)

000 -LEADER
fixed length control field 03675cam a2200565Ii 4500
001 - CONTROL NUMBER
control field 9780429342769
003 - CONTROL NUMBER IDENTIFIER
control field FlBoTFG
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220509192917.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu|||unuuu
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 191003s2020 flu ob 001 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency OCoLC-P
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency OCoLC-P
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780429342769
-- (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 0429342764
-- (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781000700039
-- (electronic bk. : PDF)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1000700038
-- (electronic bk. : PDF)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 9780367342906
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781000701258
-- (electronic bk. : EPUB)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1000701255
-- (electronic bk. : EPUB)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781000700640
-- (electronic bk. : Mobipocket)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 100070064X
-- (electronic bk. : Mobipocket)
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1121596821
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC-P)1121596821
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number RA410.6
072 #7 - SUBJECT CATEGORY CODE
Subject category code BUS
Subject category code subdivision 070080
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 000000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 012040
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UY
Source bicssc
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 362.1068/3
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Yang, Chengliang,
Relator term author.
245 10 - TITLE STATEMENT
Title Data driven approaches for healthcare :
Remainder of title machine learning for identifying high utilizers /
Statement of responsibility, etc Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka.
264 #1 -
-- Boca Raton :
-- CRC Press, Taylor & Francis Group,
-- 2020.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource.
336 ## -
-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
490 1# - SERIES STATEMENT
Series statement Chapman & Hall/CRC big data series
520 ## - SUMMARY, ETC.
Summary, etc Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients' acute and chronic condition loads and demographic characteristics
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Introduction. Overview of Healthcare Data. Machine Learning Modeling from Healthcare Data. Machine Learning Modeling from Healthcare Data. Descriptive Analysis of High Utlizers. Residuals Analysis for Identifying High Utilizers.Machine Learning Results for High Utilizers.
588 ## -
-- OCLC-licensed vendor bibliographic record.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Medical care
General subdivision Utilization
-- Mathematical models.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element BUSINESS & ECONOMICS / Industries / Service Industries
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element COMPUTERS / General
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element COMPUTERS / Computer Graphics / Game Programming & Design
Source of heading or term bisacsh
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Delcher, Chris,
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Shenkman, Elizabeth,
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ranka, Sanjay,
Relator term author.
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified Taylor & Francis
Uniform Resource Identifier https://www.taylorfrancis.com/books/9780429342769
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified OCLC metadata license agreement
Uniform Resource Identifier http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf

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