Data driven approaches for healthcare : machine learning for identifying high utilizers /: machine learning for identifying high utilizers. (2020)
- Record Type:
- Book
- Title:
- Data driven approaches for healthcare : machine learning for identifying high utilizers /: machine learning for identifying high utilizers. (2020)
- Main Title:
- Data driven approaches for healthcare : machine learning for identifying high utilizers
- Further Information:
- Note: Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka.
- Authors:
- Yang, Chengliang
Delcher, Chris
Shenkman, Elizabeth
Ranka, Sanjay - Contents:
- 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.
- Publisher Details:
- Boca Raton : CRC Press, Taylor & Francis Group
- Publication Date:
- 2020
- Extent:
- 1 online resource
- Subjects:
- 362.1068/3
Medical care -- Utilization -- Mathematical models
Machine learning
BUSINESS & ECONOMICS / Industries / Service Industries
COMPUTERS / General
COMPUTERS / Computer Graphics / Game Programming & Design
Electronic books - Languages:
- English
- ISBNs:
- 9780429342769
0429342764
9781000700039
1000700038
9781000701258
1000701255
9781000700640 - Related ISBNs:
- 9780367342906
100070064X - Notes:
- Note: Includes bibliographical references and index.
Note: Online resource; title from PDF title page (Taylor & Francis, viewed October 7, 2019). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.462261
- Ingest File:
- 02_603.xml