Early Prediction of Adverse Stroke Outcomes Using Nonclinical Factors and Missing Data: A Machine Learning Study
We demonstrate that nonclinical factors and missingness of data can assist in early predictions of 90-day adverse stroke outcomes. As these factors are often well documented in electronic health systems, they could complement or supplement traditional clinical predictive factors.
Authors
- Katryna Cisek (ORCID: https://orcid.org/0000-0002-2888-9943)
- Esra Zihni (ORCID: https://orcid.org/0000-0003-2288-2406)
- Stelios Lamprou (ORCID: https://orcid.org/0000-0001-8479-0289)
- Kalliopi Mavromati
- John D. Kelleher
- Terence J. Quinn
- Lucie Tvrda
Institutions
- Trinity College Dublin (IE)
- Technological University Dublin (IE)
- University of Glasgow (GB)
Publication Details
- Journal
- Cerebrovascular Diseases
- Published
- 2026-02-09
- DOI
- https://doi.org/10.1159/000550930
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- University of Glasgow
- HORIZON EUROPE Framework Programme