Extracting Cognitive Impairment Assessment Information From Unstructured Notes in Electronic Health Records Using Natural Language Processing Tools: Validation with Clinical Assessment Data
We developed an NLP algorithm to accurately extract cognitive scores from unstructured EHR, and these extracted cognitive scores were well correlated with cognition function recorded in CMS-mandated clinical assessments. This could help researchers identify patients with various degrees of cognitive impairment in EHR-based research.
Authors
- Cassandra York (ORCID: https://orcid.org/0000-0001-5940-0744)
- Dae Hyun Kim (ORCID: https://orcid.org/0000-0001-7290-6838)
- Lily G. Bessette (ORCID: https://orcid.org/0000-0003-1088-8579)
- Kueiyu Joshua Lin (ORCID: https://orcid.org/0000-0003-4564-3715)
- Heidi Zakoul (ORCID: https://orcid.org/0009-0008-3022-9013)
- Su Been Lee (ORCID: https://orcid.org/0000-0002-3437-097X)
- Kuan‐Yuan Wang (ORCID: https://orcid.org/0000-0002-5004-2159)
- Mufaddal Mahesri (ORCID: https://orcid.org/0000-0002-6684-2337)
- John Novoa-Laurentiev
- Zhou Li (ORCID: https://orcid.org/0000-0001-5384-3325)
- Kerry Ngan
Institutions
- Brigham and Women's Hospital (US)
- Beth Israel Deaconess Medical Center (US)
- Harvard University (US)
- Massachusetts General Hospital (US)
- Hebrew SeniorLife (US)
- National Cheng Kung University Hospital (TW)
Publication Details
- Journal
- Clinical Epidemiology
- Published
- 2025-04-01
- DOI
- https://doi.org/10.2147/clep.s504259
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- U.S. National Library of Medicine