Model-based machine learning to understand the progression of asthma throughout the life course - 05/01/26
Doi : 10.1016/j.jaci.2025.09.003
Darije Custovic, PhD a, Adnan Custovic, MD, PhD, FAAAI, FMedSci a, ⁎ 
, James Gern, MD b, c, Sejal Saglani, MD, PhD a, Sara Fontanella, PhD a
, James Gern, MD b, c, Sejal Saglani, MD, PhD a, Sara Fontanella, PhD a a National Heart and Lung Institute, Imperial College London, London, United Kingdom
b Department of Pediatrics, University of Wisconsin-Madison, Madison, Wis
c Department of Medicine, University of Wisconsin-Madison, Madison, Wis
∗ Corresponding author: Adnan Custovic, MD, PhD, FMedSci, National Heart and Lung Institute, Victor Philip Dadaleh Building, Hammersmith Hospital Campus, Imperial College London, Du Cane Road, London W12 0NN, United Kingdom. National Heart and Lung Institute Imperial College London St. Mary’s Campus Medical School London W21PG United Kingdom
Key words : Asthma, machine learning, wheeze phenotypes, asthma prediction, lung function
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| This article is part of a special issue entitled: Artificial Intelligence (AI) in Allergy/Immunology published in the Journal of Allergy and Clinical Immunology . |
© 2025
American Academy of Allergy, Asthma & Immunology. Publié par Elsevier Masson SAS. Tous droits réservés.
Vol 157 - N° 1
P. 59-61 - janvier 2026 Retour au numéro
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