NDLI: Random Forests on Ubiquitous Data for Heart Failure 30-Day Readmissions Prediction

Content Provider	IEEE Xplore Digital Library
Author	Vedomske, M.A. Brown, D.E. Harrison, J.H.
Copyright Year	2013
Description	Author affiliation: Depts. of Public Health Sci. & Pathology, Univ. of Virginia, Charlottesville, VA, USA (Harrison, J.H.) \|\| Dept. of Syst. & Inf. Eng., Univ. of Virginia, Charlottesville, VA, USA (Vedomske, M.A.; Brown, D.E.)
Abstract	Heart failure is the most common reason for unplanned hospital readmissions. Typical 30 day readmission prediction models either use data that are not readily available at the majority of US hospitals or use modeling techniques that do not provide adequate prediction accuracy. Moreover, the tendency of ongoing studies is to incorporate clinical data that is only present in the most modern electronic health record systems (EHRs). This is problematic as the population most affected by heart disease, the rural poor, is also the same population whose hospitals have the slowest adoption rates of advanced EHR systems. We apply the machine learning technique random forests to administrative claims data to predict unplanned all-cause 30 day readmissions for congestive heart failure patients in a hospital system located in central Virginia, USA. We form two random forests model variants based on datasets comprised of procedure data, diagnosis data, a combination of both, and basic demographic data. Our results show significant predictive performance, yield importance rankings for candidate variables, and address heart failure readmissions in high-need areas.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	415
Ending Page	421
File Size	433276
Page Count	7
File Format	PDF
ISBN	9780769551449
DOI	10.1109/ICMLA.2013.158
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart heart failure Sensitivity Hospitals 30 day readmission Vegetation random forests Data models health informatics Medical diagnostic imaging claims data
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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