Identifying determinants of readmission and death post-stroke using explainable machine learning
Integrating SDOH, environmental factors, and behavioral modifications alongside traditional clinical predictors enhances the predictive accuracy of post-stroke outcome models. This underscores the critical role of addressing socioeconomic disparities during post-stroke transitions of care. Moreover, XML models' ability to identify predictors spanning clinical and non-clinical domains suggests their potential to guide recovery. The resulting predictors are crucial for post-hospital care and hold strong potential for identifying individuals at risk of stroke, making them potentially significant across pre-stroke and hospitalization stages.
Authors
- K. H. Johnson (ORCID: https://orcid.org/0000-0001-9548-6004)
- Emir Veledar (ORCID: https://orcid.org/0000-0002-3831-5433)
- Carolina M Gutierrez (ORCID: https://orcid.org/0000-0002-9879-3912)
- Tatjana Rundek (ORCID: https://orcid.org/0000-0002-7115-9815)
- Víctor J. Del Brutto (ORCID: https://orcid.org/0000-0002-5115-6916)
- Omar Veledar (ORCID: https://orcid.org/0000-0002-8302-7721)
- Gillian Gordon Perue
- Ayham M. Alkhachroum (ORCID: https://orcid.org/0000-0003-0352-5913)
- David Z. Rose (ORCID: https://orcid.org/0000-0002-9449-6494)
- Negar Asdaghi (ORCID: https://orcid.org/0000-0002-0285-825X)
- José G. Romano (ORCID: https://orcid.org/0000-0003-3667-2792)
- Lili Zhou (ORCID: https://orcid.org/0000-0002-9684-5993)
- Hannah E. Gardener (ORCID: https://orcid.org/0000-0003-1416-632X)
- Farya Fakoori
- Scott C. Brown (ORCID: https://orcid.org/0000-0002-2541-5750)
Institutions
- University of Miami (US)
- University of South Florida (US)
- BEST - Bioenergy and Sustainable Technologies (Austria) (AT)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-09-18
- DOI
- https://doi.org/10.1371/journal.pone.0332371
- Citations
- 2
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 2.71