Abstract
Background: Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structured real-world evidence databases seldom include the severity. The aim of this study was to use machine learning models to impute NIHSS scores for all patients with newly diagnosed stroke from multi-institution electronic health record (EHR) data.
Original language | English (US) |
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Article number | 8 |
Journal | BMC Medical Informatics and Decision Making |
Volume | 20 |
Issue number | 1 |
DOIs | |
State | Published - Jan 8 2020 |
Externally published | Yes |
Keywords
- Database
- Outcomes research
- Real-world evidence
ASJC Scopus subject areas
- Health Policy
- Health Informatics
- Computer Science Applications