Temporal shifts in prognostic factors for 90- and 180-day outcomes after stroke thrombolysis: A machine learning analysis
Our study reveals a crucial temporal evolution in prognostic biomarkers after thrombolysis, shifting from fibrinogen at 90 days to white blood cell count at 180 days. This dynamic landscape of predictors, identified through machine learning, underscores the necessity of developing time-specific models to accurately forecast patient recovery.
Authors
- Youli Jiang (ORCID: https://orcid.org/0000-0002-6856-5177)
- Aixia Chen (ORCID: https://orcid.org/0000-0002-6198-7721)
- Huan Ye (ORCID: https://orcid.org/0000-0001-9285-1504)
- Ao Li (ORCID: https://orcid.org/0000-0002-1927-8606)
Institutions
- Central South University (CN)
- ShenZhen People’s Hospital (CN)
- Second Xiangya Hospital of Central South University (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-12-04
- DOI
- https://doi.org/10.1371/journal.pone.0338011
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00