Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 56, Issue -, Pages 229-238Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2015.05.016
Keywords
Electronic Health Records; Early readmission; Penalized methods; Random forest; Deep learning; Predictive models
Funding
- Quintiles
- National Science Foundation [DMS-1127914]
Ask authors/readers for more resources
Risk sharing arrangements between hospitals and payers together with penalties imposed by the Centers for Medicare and Medicaid (CMS) are driving an interest in decreasing early readmissions. There are a number of published risk models predicting 30 day readmissions for particular patient populations, however they often exhibit poor predictive performance and would be unsuitable for use in a clinical setting. In this work we describe and compare several predictive models, some of which have never been applied to this task and which outperform the regression methods that are typically applied in the healthcare literature. In addition, we apply methods from deep learning to the five conditions CMS is using to penalize hospitals, and offer a simple framework for determining which conditions are most cost effective to target. (C) 2015 The Authors. Published by Elsevier Inc.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available