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Article

Prediction of Hospital Readmission from Longitudinal Mobile Data Streams

1
Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA
2
Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA
3
Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA
4
Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
5
Information School, University of Washington, Seattle, WA 98105, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Loris Nanni
Sensors 2021, 21(22), 7510; https://doi.org/10.3390/s21227510
Received: 3 October 2021 / Revised: 3 November 2021 / Accepted: 8 November 2021 / Published: 12 November 2021
(This article belongs to the Special Issue Passive Sensing for Health)
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. View Full-Text
Keywords: mobile and wearable sensing; data processing; feature extraction; deep learning; patient readmission mobile and wearable sensing; data processing; feature extraction; deep learning; patient readmission
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MDPI and ACS Style

Qian, C.; Leelaprachakul, P.; Landers, M.; Low, C.; Dey, A.K.; Doryab, A. Prediction of Hospital Readmission from Longitudinal Mobile Data Streams. Sensors 2021, 21, 7510. https://doi.org/10.3390/s21227510

AMA Style

Qian C, Leelaprachakul P, Landers M, Low C, Dey AK, Doryab A. Prediction of Hospital Readmission from Longitudinal Mobile Data Streams. Sensors. 2021; 21(22):7510. https://doi.org/10.3390/s21227510

Chicago/Turabian Style

Qian, Chen, Patraporn Leelaprachakul, Matthew Landers, Carissa Low, Anind K. Dey, and Afsaneh Doryab. 2021. "Prediction of Hospital Readmission from Longitudinal Mobile Data Streams" Sensors 21, no. 22: 7510. https://doi.org/10.3390/s21227510

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