Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing
The automatic natural language processing-based model can enable large-scale stroke severity phenotyping from the electronic health record and support real-world quality improvement and comparative effectiveness studies in stroke.
Authors
- Marta Fernandes (ORCID: https://orcid.org/0000-0002-7203-2832)
- Michael Brandon Westover (ORCID: https://orcid.org/0000-0003-4803-312X)
- Sahar F. Zafar (ORCID: https://orcid.org/0000-0001-5252-5376)
- Aneesh B. Singhal (ORCID: https://orcid.org/0000-0002-2641-5277)
Institutions
- Beth Israel Deaconess Medical Center (US)
- Massachusetts General Hospital (US)
Publication Details
- Journal
- Journal of the American Heart Association
- Published
- 2024-10-25
- DOI
- https://doi.org/10.1161/jaha.124.036386
- Citations
- 3
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.16