Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction
Wearable data and multiscale entropy can improve prediction of a deep-learning model to predict unplanned 90-day readmissions. Prospective studies are needed to validate these findings.
Authors
- Vishal Nagarajan
- Shamim Nemati (ORCID: https://orcid.org/0000-0002-0520-4948)
- Gabriel Wardi (ORCID: https://orcid.org/0000-0002-4845-0217)
- Supreeth P. Shashikumar (ORCID: https://orcid.org/0000-0002-0348-4261)
- Atul Malhotra (ORCID: https://orcid.org/0000-0002-9509-1827)
Institutions
- University of California San Diego (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1093/jamia/ocae242
- Citations
- 2
- Primary Topic
- Heart Failure Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- National Institutes of Health