Prediction of Hospital Readmission from Longitudinal Mobile Data Streams

Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current appr...

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Autores principales:	Chen Qian, Patraporn Leelaprachakul, Matthew Landers, Carissa Low, Anind K. Dey, Afsaneh Doryab
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	mobile and wearable sensing data processing feature extraction deep learning patient readmission Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/865e01a99f27490ea6e853119d1bf335
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spelling	oai:doaj.org-article:865e01a99f27490ea6e853119d1bf3352021-11-25T18:57:01ZPrediction of Hospital Readmission from Longitudinal Mobile Data Streams10.3390/s212275101424-8220https://doaj.org/article/865e01a99f27490ea6e853119d1bf3352021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7510https://doaj.org/toc/1424-8220Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast which patients are likely to be readmitted. Our research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework. Through extensive experiments on a real-world dataset that includes smartphone and Fitbit device data from 49 patients collected for 60 days after discharge, we demonstrate our framework’s ability to closely simulate the readmission risk trajectories for cancer patients.Chen QianPatraporn LeelaprachakulMatthew LandersCarissa LowAnind K. DeyAfsaneh DoryabMDPI AGarticlemobile and wearable sensingdata processingfeature extractiondeep learningpatient readmissionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7510, p 7510 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mobile and wearable sensing data processing feature extraction deep learning patient readmission Chemical technology TP1-1185
spellingShingle	mobile and wearable sensing data processing feature extraction deep learning patient readmission Chemical technology TP1-1185 Chen Qian Patraporn Leelaprachakul Matthew Landers Carissa Low Anind K. Dey Afsaneh Doryab Prediction of Hospital Readmission from Longitudinal Mobile Data Streams
description	Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast which patients are likely to be readmitted. Our research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework. Through extensive experiments on a real-world dataset that includes smartphone and Fitbit device data from 49 patients collected for 60 days after discharge, we demonstrate our framework’s ability to closely simulate the readmission risk trajectories for cancer patients.
format	article
author	Chen Qian Patraporn Leelaprachakul Matthew Landers Carissa Low Anind K. Dey Afsaneh Doryab
author_facet	Chen Qian Patraporn Leelaprachakul Matthew Landers Carissa Low Anind K. Dey Afsaneh Doryab
author_sort	Chen Qian
title	Prediction of Hospital Readmission from Longitudinal Mobile Data Streams
title_short	Prediction of Hospital Readmission from Longitudinal Mobile Data Streams
title_full	Prediction of Hospital Readmission from Longitudinal Mobile Data Streams
title_fullStr	Prediction of Hospital Readmission from Longitudinal Mobile Data Streams
title_full_unstemmed	Prediction of Hospital Readmission from Longitudinal Mobile Data Streams
title_sort	prediction of hospital readmission from longitudinal mobile data streams
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/865e01a99f27490ea6e853119d1bf335
work_keys_str_mv	AT chenqian predictionofhospitalreadmissionfromlongitudinalmobiledatastreams AT patrapornleelaprachakul predictionofhospitalreadmissionfromlongitudinalmobiledatastreams AT matthewlanders predictionofhospitalreadmissionfromlongitudinalmobiledatastreams AT carissalow predictionofhospitalreadmissionfromlongitudinalmobiledatastreams AT anindkdey predictionofhospitalreadmissionfromlongitudinalmobiledatastreams AT afsanehdoryab predictionofhospitalreadmissionfromlongitudinalmobiledatastreams
_version_	1718410536763260928

Prediction of Hospital Readmission from Longitudinal Mobile Data Streams

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