Faculty, Staff and Student Publications
Publication Date
5-1-2025
Journal
Journal of Allergy and Clinical Immunology: Global
Abstract
BACKGROUND: Childhood asthma often continues into adulthood, but some children experience remission. Utilizing electronic health records (EHRs) to predict asthma prognosis can aid health care providers and patients in developing effective prioritized care plans.
OBJECTIVE: We aimed to develop artificial intelligence (AI) models using various clinical variables extracted from EHRs to predict childhood asthma prognosis (remission vs no remission) in different age groups.
METHODS: We developed AI models utilizing patients' EHRs during the first 6, 9, or 12 years of their lives to predict their asthma prognosis status at ages 6 to 9, 9 to 12, or 12 to 15 years, respectively. We first developed the models based on a manually annotated birth cohort (n = 900). We then leveraged a larger birth cohort (n = 29,594) labeled automatically (with weak labels) by a previously validated natural language processing algorithm for asthma prognosis. Different models (logistic regression, random forest, and XGBoost [eXtreme Gradient Boosting]) were tested with diverse clinical variables from structured and unstructured EHRs.
RESULTS: The best AI models of each age group produced a prediction performance with areas under the receiver operating characteristic curve ranging from 0.85 to 0.93. The prediction model at age 12 showed the highest performance. Most of the AI models with weak labels showed enhanced performance, and models using the top 10 variables performed similarly to those using all of the variables.
CONCLUSIONS: The AI models effectively predicted asthma prognosis for children by using EHRs with a relatively small number of variables. This approach demonstrates the potential to enhance prioritized care plans and patient education, improving disease management and quality of life for asthmatic patients.
Keywords
Asthma, asthma prognosis, natural language processing, artificial intelligence, machine learning, electronic health records, dynamic variables
DOI
10.1016/j.jacig.2025.100429
PMID
40091884
PMCID
PMC11908553
PubMedCentral® Posted Date
1-31-2025
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Allergy and Immunology Commons, Artificial Intelligence and Robotics Commons, Bioinformatics Commons, Biological Phenomena, Cell Phenomena, and Immunity Commons, Biomedical Informatics Commons, Medical Immunology Commons