Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records.
Saved in:
| Title: | Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records. |
|---|---|
| Authors: | Bağcı MF; Department 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, Calif., Do T; Department of Allergy & Immunology, University of California San Diego School of Medicine, San Diego, Calif., Spierling Bagsic SR; Department of Research Development, Scripps Health, San Diego, Calif., Gomez RF; Department of Knowledge Management, Scripps Health, San Diego, Calif., Jun JH; Department of Knowledge Management, Scripps Health, San Diego, Calif., Ritko AL; Department of Internal Medicine, Scripps Health, San Diego, Calif., Wenzel SE; University of Pittsburgh, Pittsburgh, Pa., Nguyen T; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, Calif., Öztürk Y; Department of Electrical and Computer Engineering, San Diego State University, San Diego, Calif., Modena BD; Modena Allergy + Asthma, La Jolla, Calif. |
| Source: | The journal of allergy and clinical immunology. Global [J Allergy Clin Immunol Glob] 2025 Apr 17; Vol. 4 (3), pp. 100473. Date of Electronic Publication: 2025 Apr 17 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Elsevier Inc Country of Publication: United States NLM ID: 9918453488706676 Publication Model: eCollection Cited Medium: Internet ISSN: 2772-8293 (Electronic) Linking ISSN: 27728293 NLM ISO Abbreviation: J Allergy Clin Immunol Glob Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
Be the first to leave a comment!
