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Predicting Unplanned Hospital Readmissions using Patient Level Data

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Title:Predicting Unplanned Hospital Readmissions using Patient Level Data
Authors:Balan U, Mahesh
Gandhi, Meet
Rammohan, Swaminathan
Keywords:Big Data on Healthcare Application
deep learning
embeddings
healthcare
readmissions
show 1 moresequence models
show less
Date Issued:05 Jan 2021
Abstract:The rate of unplanned hospital readmissions in the US is likely to face a steady rise after 2020. Hence, this issue has received considerable critical attention with the policy makers. Majority of hospitals in the US pay millions of dollars as penalty for readmitting patients within 30 days due to strict norms imposed by the Hospital Readmission Reduction Program. In this study, we develop two novel models: PURE (Predicting Unplanned Readmissions using Embeddings) and Hybrid DeepR, which uses the historical medical events of patients to predict readmissions within 30 days. Both these models are hybrid sequence models that leverage both sequential events (history of events) and static features (like gender, blood pressure) of the patients to mine patterns in the data. Our results are promising, and they benchmark previous results in predicting hospital readmissions. The contributions of this study add to existing literature on healthcare analytics.
Pages/Duration:9 pages
URI:http://hdl.handle.net/10125/71033
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.417
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Big Data on Healthcare Application


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