Identifying Patients With Delirium Based on Unstructured Clinical Notes: Observational Study
JMIR Formative Research2022Vol. 6(6), pp. e33834–e33834
Citations Over TimeTop 10% of 2022 papers
Wendong Ge, Haitham Alabsi, Aayushee Jain, Elissa Ye, Haoqi Sun, Marta Fernandes, Colin Magdamo, Ryan A. Tesh, Sarah Isabel Collens, Amy Newhouse, Lidia M.V.R. Moura, Sahar F. Zafar, John Hsu, Oluwaseun Akeju, Gregory K. Robbins, Shibani S. Mukerji, Sudeshna Das, M. Brandon Westover
Abstract
Clinical notes are an attractive alternative to ICD codes for EHR delirium studies but require automated methods. Our NLP model detects delirium with high accuracy, similar to manual chart review. Our NLP approach can provide more accurate determination of delirium for large-scale EHR-based studies regarding delirium, quality improvement, and clinical trails.
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