Machine learning tool improves pediatricians’ ability to identify children at risk for persistent asthma
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Pilot randomized trial finds EHR-integrated decision support improved clinicians’ prediction of school-age asthma
Jul 14, 2026
A machine learning tool that analyzes information already captured in a child’s electronic health record helped pediatricians more accurately assess asthma risk in standardized clinical case scenarios, according to a pilot randomized clinical trial led by a Regenstrief Institute researcher. The study was published in Scientific Reports.
The study evaluated a machine learning-enabled clinical decision support tool called the Passive Digital Marker, which uses routinely collected EHR data to classify young children as having a high or low risk of developing persistent asthma.
Asthma is one of the most common chronic childhood diseases, but predicting which young children with wheezing or other respiratory symptoms will go on to develop persistent asthma remains difficult. While some children outgrow these symptoms, others require ongoing treatment, making early risk assessment an important but challenging part of pediatric care.
“This tool doesn’t replace a pediatrician’s clinical judgment,” said Arthur H. Owora, PhD, Regenstrief Institute research scientist and lead author of the study. “It helps bring together years of clinical information that’s already in the electronic health record, giving clinicians another source of information when making decisions about a child’s asthma risk.”
Unlike many prediction tools, the Passive Digital Marker requires no additional testing or questionnaires. Instead, it analyzes information already documented in the EHR, including respiratory symptoms, allergies, medication history, respiratory infections and family history, then provides clinicians with a simple high- or low-risk assessment.
Pediatricians using the tool correctly predicted future asthma more often than those using standard assessment alone, achieving an average accuracy of 83% compared with 61%. The improvement was largely driven by better identification of children who later developed persistent asthma.
The researchers emphasize that the tool is designed to support clinical decision-making, not replace it, by helping clinicians quickly synthesize years of patient information into an easy-to-interpret risk assessment.
Because the study was conducted using standardized patient cases rather than real-world clinical encounters, additional research is needed to determine whether the tool improves patient outcomes in everyday pediatric practice.
The study was supported in part by the National Institutes of Health under grant K01HL166436.
In addition to Owora, the study was co-authored by Bowen Jiang, M.S., and Yash Shah, M.S., of the Division of Pediatric Pulmonology, Allergy/Immunology and
Sleep Medicine, Department of Pediatrics, Riley Hospital for Children, Indiana University School of Medicine.
Arthur H. Owora, PhD, MPH
Dr. Arthur H. Owora is a translational scientist, applied biostatistician and quantitative epidemiologist. His research focuses on the development, adaptation, and application of statistical and machine learning methods to address health-related questions through the use of systematic and data-driven approaches for understanding the disease etiology, risk, and prognosis.
Link to the original news article: https://www.regenstrief.org/article/machine-learning-tool-identifies-children-risk-persistent-asthma/
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