Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records
Integrating machine learning with EHRs successfully classified and identified real-world asthma phenotypes, demonstrating the potential of this approach to identify SA for appropriate management and/or clinical studies.
Authors
- Rahul F Gomez
- Yusuf Öztürk (ORCID: https://orcid.org/0000-0002-1838-6322)
- Đỗ Thị Thanh Toàn (ORCID: https://orcid.org/0000-0002-7049-7361)
- Brian D. Modena (ORCID: https://orcid.org/0000-0002-4406-8336)
- Samantha R. Spierling Bagsic (ORCID: https://orcid.org/0000-0003-1274-3736)
- Anna L Ritko
- Sally E. Wenzel (ORCID: https://orcid.org/0000-0001-7761-9109)
- Truong Nguyen (ORCID: https://orcid.org/0000-0002-2631-2446)
- JUDY JUN
- Mehmet F Bagci
Institutions
- Scripps Health (US)
- University of San Diego (US)
- University of Pittsburgh (US)
- San Diego State University (US)
- University of California San Diego (US)
- Asthma Association (BG)
Publication Details
- Journal
- Journal of Allergy and Clinical Immunology Global
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1016/j.jacig.2025.100473
- Citations
- 5
- Primary Topic
- Asthma and respiratory diseases
- Type
- article
- Field-Weighted Citation Impact
- 5.82
Funders
- GlaxoSmithKline