Journal
SENSORS
Volume 21, Issue 22, Pages -Publisher
MDPI
DOI: 10.3390/s21227510
Keywords
mobile and wearable sensing; data processing; feature extraction; deep learning; patient readmission
Funding
- Center for Machine Learning and Health at Carnegie Mellon University through the Pittsburgh Health Data Alliance
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Hospital readmissions pose challenges for healthcare systems and patients, accurate prediction is difficult, but this research shows promise in leveraging mobile data for daily risk prediction after discharge.
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.
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