Evaluation of falls detected by natural language processing algorithm and not coded external cause of morbidity
If the causal relationships between falls and comorbid conditions are not considered in NLP algorithms, they can easily identify patients who fell, but the fall was a sequela of underlying medical illness.
Authors
- Amy L. Cochran (ORCID: https://orcid.org/0000-0001-6024-796X)
- Manish N. Shah (ORCID: https://orcid.org/0000-0001-6331-1074)
- Brian W. Patterson (ORCID: https://orcid.org/0000-0002-4584-3808)
- Apoorva Maru (ORCID: https://orcid.org/0000-0001-6184-6446)
- Daniel J Hekman (ORCID: https://orcid.org/0000-0002-7964-9501)
- Erkin Ötleş (ORCID: https://orcid.org/0000-0003-3169-6832)
- Hanna J. Barton (ORCID: https://orcid.org/0000-0003-0457-1153)
- Douglas A. Wiegmann (ORCID: https://orcid.org/0000-0002-5604-9853)
Institutions
- University of Wisconsin–Madison (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf047
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Health and Human Services
- Agency for Healthcare Research and Quality
- U.S. Public Health Service