MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04185cam a2200589 i 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
20987519 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240819170336.0 |
| 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 |
9781032088686 |
| 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 |
610.285 YAN 2020 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Yang, Chengliang |
| Titles and words associated with a name |
(Of University of Florida), |
| Relator term |
author. |
| 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. |
| 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 |
| 650 22 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Medical Overuse |
| General subdivision |
statistics & numerical data |
| 650 22 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Models, Theoretical |
| 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 |
| 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 |
| 651 #2 - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME |
|---|
| Geographic name |
United States |
| 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. |
| 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. |
| 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 |
Dewey Decimal Classification |
| Koha item type |
Book |
| Suppress in OPAC |
No |
