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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/22/7510 |
| _version_ | 1827675445911355392 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:05:06Z |
| format | Article |
| id | doaj.art-865e01a99f27490ea6e853119d1bf335 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:05:06Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-865e01a99f27490ea6e853119d1bf3352023-11-23T01:24:34ZengMDPI AGSensors1424-82202021-11-012122751010.3390/s21227510Prediction of Hospital Readmission from Longitudinal Mobile Data StreamsChen Qian0Patraporn Leelaprachakul1Matthew Landers2Carissa Low3Anind K. Dey4Afsaneh Doryab5Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USAHeinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USADepartment of Computer Science, University of Virginia, Charlottesville, VA 22904, USADepartment of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USAInformation School, University of Washington, Seattle, WA 98105, USADepartment of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USAHospital 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.https://www.mdpi.com/1424-8220/21/22/7510mobile and wearable sensingdata processingfeature extractiondeep learningpatient readmission |
| spellingShingle | Chen Qian Patraporn Leelaprachakul Matthew Landers Carissa Low Anind K. Dey Afsaneh Doryab Prediction of Hospital Readmission from Longitudinal Mobile Data Streams Sensors mobile and wearable sensing data processing feature extraction deep learning patient readmission |
| title | 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_short | Prediction of Hospital Readmission from Longitudinal Mobile Data Streams |
| title_sort | prediction of hospital readmission from longitudinal mobile data streams |
| topic | mobile and wearable sensing data processing feature extraction deep learning patient readmission |
| url | https://www.mdpi.com/1424-8220/21/22/7510 |
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