Survival models and longitudinal medical events for hospital readmission forecasting
Sequences of administrative medical codes contain rich predictive information for forecasting readmissions, and embedding medical knowledge a priori using machine learning provides readmission models an advantageous foundation for training. When combined with a model that can leverage a time-to-event target, excellent performance is possible on the 30-day all-cause readmission task using only administrative data.
Authors
- Russell Greiner (ORCID: https://orcid.org/0000-0001-8327-934X)
- Sacha E. Davis (ORCID: https://orcid.org/0000-0003-0827-1932)
Institutions
- University of Alberta (CA)
- Institute of Health Economics (CA)
Publication Details
- Journal
- BMC Health Services Research
- Published
- 2024-11-13
- DOI
- https://doi.org/10.1186/s12913-024-11771-w
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- Canadian Institute for Advanced Research
- Alberta Machine Intelligence Institute
- Natural Sciences and Engineering Research Council of Canada