Identifying Asthma-Related Symptoms From Electronic Health Records Using a Hybrid Natural Language Processing Approach Within a Large Integrated Health Care System: Retrospective Study
The developed NLP algorithms could effectively capture asthma-related symptoms from unstructured clinical notes. These algorithms could be used to facilitate early asthma detection and predict exacerbation risk.
Authors
- Michael Schätz (ORCID: https://orcid.org/0000-0002-7640-5560)
- Wansu Chen (ORCID: https://orcid.org/0000-0002-3486-7468)
- William M. Vollmer (ORCID: https://orcid.org/0000-0003-1216-323X)
- Fagen Xie (ORCID: https://orcid.org/0000-0002-1565-0490)
- Robert S. Zeiger (ORCID: https://orcid.org/0000-0001-5788-5063)
- Eric J. Puttock (ORCID: https://orcid.org/0000-0003-3963-314X)
- William Crawford (ORCID: https://orcid.org/0000-0001-8447-7532)
- Stanley Xu (ORCID: https://orcid.org/0000-0002-4750-7672)
- Mary Saparudin
- Sahar Al-Salman
Institutions
- Kaiser Permanente (US)
- Kaiser Permanente San Diego Medical Center (US)
- Kaiser Permanente Center for Health Research (US)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-03-15
- DOI
- https://doi.org/10.2196/69132
- Citations
- 5
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 12.40