Data-driven approaches for health care : (Record no. 594800)

000 -LEADER
fixed length control field 04225cam a2200565 i 4500
001 - CONTROL NUMBER
control field 20987519
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230518101752.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190528s2020 flua b 001 0 eng c
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2019941841
016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER
Record control number 101763352
Source DNLM
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780367342906
Qualifying information (hardback ;
-- alk. paper)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 0367342901
Qualifying information (hardback ;
-- alk. paper)
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)on1102647135
040 ## - CATALOGING SOURCE
Original cataloging agency NLM
Language of cataloging eng
Transcribing agency NLM
Description conventions rda
Modifying agency YDXIT
-- OCLCF
-- NUI
-- YDX
-- OCLCO
-- OCLCQ
-- OCLCA
-- UPM
-- OCLCO
-- DLC
042 ## - AUTHENTICATION CODE
Authentication code pcc
043 ## - GEOGRAPHIC AREA CODE
Geographic area code n-us---
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number RA410.6
Item number .Y36 2020
060 00 - NATIONAL LIBRARY OF MEDICINE CALL NUMBER
Classification number W 86
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 362.1068
Author Mark YAN
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Yang, Chengliang
Titles and words associated with a name (Of University of Florida),
Relator term author.
9 (RLIN) 112416
245 10 - TITLE STATEMENT
Title Data-driven approaches for health care :
Remainder of title machine learning for identifying high utilizers /
Statement of responsibility, etc. Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Boca Raton :
Name of producer, publisher, distributor, manufacturer CRC Press,
Date of production, publication, distribution, manufacture, or copyright notice [2020]
300 ## - PHYSICAL DESCRIPTION
Extent ix, 107 pages :
Other physical details illustrations ;
Dimensions 26 cm
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term unmediated
Media type code n
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term volume
Carrier type code nc
Source rdacarrier
490 1# - SERIES STATEMENT
Series statement Chapman & Hall/CRC big data series
500 ## - GENERAL NOTE
General note "A Chapman & Hall book."
500 ## - GENERAL NOTE
General note Introduction. Overview of Healthcare Data. Machine Learning Modeling from Healthcare Data. Machine Learning Modeling from Healthcare Data. Descriptive Analysis of High Utilizers. Residuals Analysis for Identifying High Utilizers. Machine Learning Results for High Utilizers.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references and index.
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.
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.--
Assigning source Source other than the Library of Congress.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Medical care
General subdivision Utilization
-- Mathematical models.
9 (RLIN) 112417
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
650 12 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Medical Overuse
General subdivision prevention & control
9 (RLIN) 112418
650 22 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Medical Overuse
General subdivision statistics & numerical data
9 (RLIN) 112419
650 22 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Models, Theoretical
9 (RLIN) 2968
650 22 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine Learning
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
Source of heading or term fast
Authority record control number or standard number (OCoLC)fst01004795
9 (RLIN) 112420
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Medical care
General subdivision Utilization
-- Mathematical models.
Source of heading or term fast
Authority record control number or standard number (OCoLC)fst01013885
9 (RLIN) 112417
651 #2 - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME
Geographic name United States
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Delcher, Chris,
Relator term author.
9 (RLIN) 112421
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Shenkman, Elizabeth,
Relator term author.
9 (RLIN) 112422
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ranka, Sanjay,
Relator term author.
9 (RLIN) 101224
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Electronic version:
Main entry heading Yang, Chengliang.
Title Data driven approaches for healthcare.
Place, publisher, and date of publication Boca Raton : CRC Press, Taylor & Francis Group, 2020
International Standard Book Number 9780429342769
Record control number (OCoLC)1121596821
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Chapman & Hall/CRC big data series.
9 (RLIN) 112423
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a 7
b cbc
c pccadap
d 2
e ncip
f 20
g y-gencatlg
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
          School of Mechanical & Manufacturing Engineering (SMME) School of Mechanical & Manufacturing Engineering (SMME) General Stacks 05/18/2023   362.1068 YAN SMME-4394 05/18/2023 05/18/2023 Book
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