This paper is in the following e-collection/theme issue:

NIH mHealth - funded projects (82) Clinical Informatics (1417) Machine Learning (2202) Clinical Information and Decision Making (2043) Asthma Education and Self-Management in Childhood and Adolescence (91)

Published on in Vol 13 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57981, first published .
Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study

Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study

Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study

Authors of this article:

Jie Xu1 Author Orcid Image ;   Sankalp Talankar1 Author Orcid Image ;   Jinqian Pan1 Author Orcid Image ;   Ira Harmon2 Author Orcid Image ;   Yonghui Wu1 Author Orcid Image ;   David A Fedele3 Author Orcid Image ;   Jennifer Brailsford2 Author Orcid Image ;   Jennifer Noel Fishe2, 4 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Pan J, Xu J, Fedele D, Wu Y, Brailsford J, Fishe J. Determination of pediatric asthma severity using retrospective electronic health record prescription data: an analysis before and after the 2020 national asthma education and prevention program guideline update. Journal of Asthma 2025;62(10):1729 View
  2. Arnau-Soler A, Corriger J, Chantran Y, Goret J. Improving machine-learning development in allergology: bridging the gap between open-access and cohort-based databases. Current Opinion in Allergy & Clinical Immunology 2026;26(2):138 View

Conference Proceedings

  1. Vasekar V, Ahire P. 2025 9th International Conference on Computing, Communication, Control and Automation (ICCCBEA). Towards Privacy-Preserving AI: Exploring Federated Learning Frameworks and Datasets View