Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults-Reference-Cited by-同舟云学术

Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults

Published:2019-03-06 Issue:3 Volume:14 Page:e0213258
ISSN:1932-6203
Container-title:PLOS ONE
language:en
Short-container-title:PLoS ONE

Author:

Kan Hong J.^ORCID,Kharrazi Hadi^ORCID,Chang Hsien-Yen^ORCID,Bodycombe Dave^ORCID,Lemke Klaus,Weiner Jonathan P.

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference32 articles.

1. Risk adjustment for measuring healthcare outcomes;L Iezzoni,2012

2. The HHS-HCC risk adjustment model for individual and small group markets under the Affordable Care Act;J Kautter;Medicare & Medicaid Research Review,2014

3. A machine learning framework for plan payment risk adjustment;S Rose;Health Serv Res,2016

4. Testing alternative regression frameworks for predictive modeling of health care costs;I Duncan;North American Actuarial Journal,1996

5. Predicting patient ‘cost blooms’ in Denmark: a longitudinal population-based study;S Tamang;BMJ Open,2017

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