Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records
Background: Asthma is a heterogeneous disease with a diverse array of phenotypes that differ in inflammatory characteristics and severity. Identifying and classifying phenotypes in the real world could provide a foundation to improve and personalize asthma management. Leveraging machine learning in...
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| Format: | Article |
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Elsevier
2025-08-01
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| Series: | Journal of Allergy and Clinical Immunology: Global |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772829325000748 |
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| author | Mehmet Furkan Bağcı, MSc Toan Do, MD Samantha R. Spierling Bagsic, PhD Rahul F. Gomez, MD Judy H. Jun, MD Anna L. Ritko, MA, MPhil Sally E. Wenzel, MD Truong Nguyen, PhD Yusuf Öztürk, PhD Brian D. Modena, MD, MSc |
| author_facet | Mehmet Furkan Bağcı, MSc Toan Do, MD Samantha R. Spierling Bagsic, PhD Rahul F. Gomez, MD Judy H. Jun, MD Anna L. Ritko, MA, MPhil Sally E. Wenzel, MD Truong Nguyen, PhD Yusuf Öztürk, PhD Brian D. Modena, MD, MSc |
| author_sort | Mehmet Furkan Bağcı, MSc |
| collection | DOAJ |
| description | Background: Asthma is a heterogeneous disease with a diverse array of phenotypes that differ in inflammatory characteristics and severity. Identifying and classifying phenotypes in the real world could provide a foundation to improve and personalize asthma management. Leveraging machine learning in analyzing electronic health records (EHRs) provides an opportunity to identify real-world asthma phenotypes. Objective: We utilized machine-learning techniques applied to EHRs to detect and predict real-world severe asthma (SA) phenotypes and improve the precision of asthma severity diagnoses. Methods: Data from 31,795 asthma patients were extracted from a health care system’s EHR, with 1,112 patients meeting inclusion criteria for analysis. Principal component analysis (PCA) and a Gaussian mixture model classified patients into subject clusters (SCs). Asthma severity was assessed using two predictive models, one based on the American Thoracic Society (ATS) definition and the other a supervised model trained on 50 randomly selected patients whose disease severity was predetermined by 2 independent physicians. Results: Three principal components (PCs) emerged, reflecting lung function (PC1), blood inflammatory markers (PC2), and systemic corticosteroid receipt (PC3). PCA identified 5 distinct asthma phenotypes with significant clinical, physiologic, and inflammatory differences. A supervised model, trained on 50 randomly selected patients, predicted SA with 92% precision and 85% accuracy. SC3 was classified as an inflammatory, SA phenotype, making it highly suitable for biologic therapy. Conclusion: 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. |
| format | Article |
| id | doaj-art-130964f68fef464c9f800eb794182b8f |
| institution | DOAJ |
| issn | 2772-8293 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Allergy and Clinical Immunology: Global |
| spelling | doaj-art-130964f68fef464c9f800eb794182b8f2025-08-20T03:18:15ZengElsevierJournal of Allergy and Clinical Immunology: Global2772-82932025-08-014310047310.1016/j.jacig.2025.100473Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health recordsMehmet Furkan Bağcı, MSc0Toan Do, MD1Samantha R. Spierling Bagsic, PhD2Rahul F. Gomez, MD3Judy H. Jun, MD4Anna L. Ritko, MA, MPhil5Sally E. Wenzel, MD6Truong Nguyen, PhD7Yusuf Öztürk, PhD8Brian D. Modena, MD, MSc9Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, Calif; Department of Electrical and Computer Engineering, San Diego State University, San Diego, CalifDepartment of Allergy & Immunology, University of California San Diego School of Medicine, San Diego, CalifDepartment of Research Development, Scripps Health, San Diego, CalifDepartment of Knowledge Management, Scripps Health, San Diego, CalifDepartment of Knowledge Management, Scripps Health, San Diego, CalifDepartment of Internal Medicine, Scripps Health, San Diego, CalifUniversity of Pittsburgh, Pittsburgh, PaDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CalifDepartment of Electrical and Computer Engineering, San Diego State University, San Diego, Calif; Corresponding author: Yusuf Öztürk, PhD, Smart Health Institute, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182-1309.Modena Allergy + Asthma, La Jolla, CalifBackground: Asthma is a heterogeneous disease with a diverse array of phenotypes that differ in inflammatory characteristics and severity. Identifying and classifying phenotypes in the real world could provide a foundation to improve and personalize asthma management. Leveraging machine learning in analyzing electronic health records (EHRs) provides an opportunity to identify real-world asthma phenotypes. Objective: We utilized machine-learning techniques applied to EHRs to detect and predict real-world severe asthma (SA) phenotypes and improve the precision of asthma severity diagnoses. Methods: Data from 31,795 asthma patients were extracted from a health care system’s EHR, with 1,112 patients meeting inclusion criteria for analysis. Principal component analysis (PCA) and a Gaussian mixture model classified patients into subject clusters (SCs). Asthma severity was assessed using two predictive models, one based on the American Thoracic Society (ATS) definition and the other a supervised model trained on 50 randomly selected patients whose disease severity was predetermined by 2 independent physicians. Results: Three principal components (PCs) emerged, reflecting lung function (PC1), blood inflammatory markers (PC2), and systemic corticosteroid receipt (PC3). PCA identified 5 distinct asthma phenotypes with significant clinical, physiologic, and inflammatory differences. A supervised model, trained on 50 randomly selected patients, predicted SA with 92% precision and 85% accuracy. SC3 was classified as an inflammatory, SA phenotype, making it highly suitable for biologic therapy. Conclusion: 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.http://www.sciencedirect.com/science/article/pii/S2772829325000748Asthmamachine learningelectronic health recordspredictive modeling |
| spellingShingle | Mehmet Furkan Bağcı, MSc Toan Do, MD Samantha R. Spierling Bagsic, PhD Rahul F. Gomez, MD Judy H. Jun, MD Anna L. Ritko, MA, MPhil Sally E. Wenzel, MD Truong Nguyen, PhD Yusuf Öztürk, PhD Brian D. Modena, MD, MSc Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records Journal of Allergy and Clinical Immunology: Global Asthma machine learning electronic health records predictive modeling |
| title | Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records |
| title_full | Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records |
| title_fullStr | Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records |
| title_full_unstemmed | Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records |
| title_short | Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records |
| title_sort | detection and prediction of real world severe asthma phenotypes by application of machine learning to electronic health records |
| topic | Asthma machine learning electronic health records predictive modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2772829325000748 |
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