Predicting Unplanned Hospital Readmissions using Patient Level Data

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dc.contributor.authorBalan U, Mahesh
dc.contributor.authorGandhi, Meet
dc.contributor.authorRammohan, Swaminathan
dc.date.accessioned2020-12-24T19:42:22Z
dc.date.available2020-12-24T19:42:22Z
dc.date.issued2021-01-05
dc.description.abstractThe 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.
dc.format.extent9 pages
dc.identifier.doi10.24251/HICSS.2021.417
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/71033
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data on Healthcare Application
dc.subjectdeep learning
dc.subjectembeddings
dc.subjecthealthcare
dc.subjectreadmissions
dc.subjectsequence models
dc.titlePredicting Unplanned Hospital Readmissions using Patient Level Data
prism.startingpage3436

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