Predicting 30-day risk and cost of "all-cause" hospital readmissions

Shanu Sushmita, Garima Khulbe, Aftab Hasan, Stacey Newman, Padmashree Ravindra, Senjuti Basu Roy, Martine De Cock, Ankur Teredesai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

The hospital readmission rate of patients within 30 days after discharge is broadly accepted as a healthcare quality measure and cost driver in the United States. The ability to estimate hospitalization costs alongside 30 day risk-stratification for such readmissions provides additional benefit for accountable care, now a global issue and foundation for the U.S. government mandate under the Affordable Care Act. Recent data mining efforts either predict healthcare costs or risk of hospital readmission, but not both. In this paper we present a dual predictive modeling effort that utilizes healthcare data to predict the risk and cost of any hospital readmission ("all-cause"). For this purpose, we explore machine learning algorithms to do accurate predictions of healthcare costs and risk of 30-day readmission. Results on risk prediction for "all-cause" readmission compared to the standardized readmission tool (LACE) are promising, and the proposed techniques for cost prediction consistently outperform baseline models and demonstrate substantially lower mean absolute error (MAE).

Original languageEnglish (US)
Title of host publicationWS-16-01
Subtitle of host publicationArtificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence
PublisherAI Access Foundation
Pages453-461
Number of pages9
ISBN (Electronic)9781577357599
StatePublished - Jan 1 2016
Externally publishedYes
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: Feb 12 2016Feb 17 2016

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-16-01 - WS-16-15

Other

Other30th AAAI Conference on Artificial Intelligence, AAAI 2016
CountryUnited States
CityPhoenix
Period2/12/162/17/16

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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