AI for pRedicting Exacerbations in KIDs with aSthma (AIRE-KIDS)

[2511.01018] AI for pRedicting Exacerbations in KIDs with aSthma (AIRE-KIDS) Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2511.01018 (cs) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 2 Nov 2025] Title: AI for pRedicting Exacerbations in KIDs with aSthma (AIRE-KIDS) Authors: Hui-Lee Ooi , Nicholas Mitsakakis , Margerie Huet Dastarac , Roger Zemek , Amy C. Plint , Jeff Gilchrist , Khaled El Emam , Dhenuka Radhakrishnan View a PDF of the paper titled AI for pRedicting Exacerbations in KIDs with aSthma (AIRE-KIDS), by Hui-Lee Ooi and 7 other authors View PDF Abstract: Recurrent exacerbations remain a common yet preventable outcome for many children with asthma. Machine learning (ML) algorithms using electronic medical records (EMR) could allow accurate identification of children at risk for exacerbations and facilitate referral for preventative comprehensive care to avoid this morbidity. We developed ML algorithms to predict repeat severe exacerbations (i.e. asthma-related emergency department (ED) visits or future hospital admissions) for children with a prior asthma ED visit at a tertiary care children's hospital. Retrospective pre-COVID19 (Feb 2017 - Feb 2019, N=2716) Epic EMR data from the Children's Hospital of Eastern Ontario (CHEO) linked with environmental pollutant exposure and neighbourhood marginalization information was used to train various ML models. We used boosted trees (LGBM, XGB) and 3 open-source large language model (LLM) approaches (DistilGPT2, Llama 3.2 1B and Llama-8b-UltraMedical). Models were tuned and calibrated then validated in a second retrospective post-COVID19 dataset (Jul 2022 - Apr 2023, N=1237) from CHEO. Models were compared using the area under the curve (AUC) and F1 scores, with SHAP values used to determine the most predictive features. The LGBM ML model performed best with the most predictive features in the final AIRE-KIDS_ED model including prior asthma ED visit, the Canadian triage acuity scale, medical complexity, food allergy, prior ED visits for non-asthma respiratory diagnoses, and age for an AUC of 0.712, and F1 score of 0.51. This is a nontrivial improvement over the current decision rule which has F1=0.334. While the most predictive features in the AIRE-KIDS_HOSP model included medical complexity, prior asthma ED visit, average wait time in the ED, the pediatric respiratory assessment measure score at triage and food allergy. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2511.01018 [cs.AI] (or arXiv:2511.01018v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2511.01018 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Ooi, HL., Mitsakakis, N., Dastarac, M.H. et al. AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS). npj Digit. Med. (2026) Related DOI : https://doi.org/10.1038/s41746-026-02824-x Focus to learn more DOI(s) linking to related resources Submission history From: Khaled El Emam [ view email ] [v1] Sun, 2 Nov 2025 17:22:44 UTC (1,652 KB) Full-text links: Access Paper: View a PDF of the paper titled AI for pRedicting Exacerbations in KIDs with aSthma (AIRE-KIDS), by Hui-Lee Ooi and 7 other authors View PDF view license Current browse context: cs.AI < prev | next > new | recent | 2025-11 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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