Machine Learning Tool Helps Paediatricians Identify Children at Risk of Persistent Asthma
Key Takeaways:
- A machine learning tool that reads data already held in a child’s electronic health record helped paediatricians more accurately judge which young children are at risk of persistent asthma.
- In a pilot randomised trial using standardised clinical cases, clinicians using the tool reached an average accuracy of 83%, compared with 61% for standard assessment alone.
- The tool is designed to support clinical judgement rather than replace it, and requires no additional tests or questionnaires.
Support for a difficult clinical judgement
A machine learning tool that analyses information already captured in a child’s electronic health record (EHR) has helped paediatricians assess asthma risk more accurately in standardised clinical case scenarios, according to a pilot randomised clinical trial led by a researcher at the Regenstrief Institute. The study was published in the journal Scientific Reports.
The trial evaluated a machine learning-enabled clinical decision support tool known as the Passive Digital Marker. The tool draws on routinely collected EHR data to classify young children as having either a high or a low risk of going on to develop persistent asthma.
Why early asthma risk is hard to predict
Asthma is one of the most common long-term conditions of childhood, yet predicting which young children who have wheezing or other respiratory symptoms will later develop persistent asthma remains difficult. Some children outgrow their early symptoms, while others need ongoing treatment. That uncertainty makes early risk assessment an important, but genuinely challenging, part of paediatric 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.”
How the Passive Digital Marker works
Unlike many prediction tools, the Passive Digital Marker requires no extra testing and asks families to complete no additional questionnaires. Instead, it analyses information that has already been documented in the child’s EHR, including respiratory symptoms, allergies, medication history, respiratory infections and family history. It then presents clinicians with a straightforward high-risk or low-risk assessment.
This approach is intended to save clinicians’ time and reduce the burden on families, since it relies on data that has been gathered over the course of a child’s routine care rather than requiring anything new at the point of decision.
What the trial found
Paediatricians using the tool correctly predicted future asthma more often than those relying on standard assessment alone, achieving an average accuracy of 83% compared with 61%. The improvement was largely driven by better identification of children who went on to develop persistent asthma – the group that is most important to recognise early and hardest to spot.
The researchers stress that the tool is meant to support clinical decision-making, not to supplant it. Its value lies in helping clinicians quickly synthesise years of patient information into a single, easy-to-interpret risk assessment that sits alongside their own expertise. That distinction – between having an AI tool to hand and knowing how to weigh what it tells you – is becoming central to how clinicians are expected to work with these systems.
Limitations and next steps
Because the study used standardised patient cases rather than real-world clinical encounters, further research is needed to establish whether the tool improves outcomes for children in everyday paediatric practice. The pilot demonstrates promise in a controlled setting, but real-world validation is the necessary next stage before wider adoption.
CCH insight
Tools like the Passive Digital Marker are only ever as good as a clinician’s ability to judge when to lean on them and when to look again. That skill – evaluating an AI tool, recognising where it can mislead, and putting sensible governance around its use – is exactly what our short course AI Essentials for GPs: Tools, Ethics and Everyday Applications is designed to build. It’s a 3.5-hour, fully online CPD course led by Prof. Mike Bewick and Dr Dipesh Naik. [Explore the course →]
Funding and authorship
The study was supported in part by the National Institutes of Health under grant K01HL166436. In addition to Owora, it 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.
Source: Regenstrief Institute
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AI Model Improves Early Detection of Serious Lung Disease in Newborns
Key Takeaways:
- Researchers at the University of Rochester have developed a time-series AI machine learning model that predicts bronchopulmonary dysplasia (BPD) in premature newborns more accurately than existing prediction tools.
- The model uses detailed electronic health record data rather than the limited datasets used in many current online BPD calculators.
- Researchers hope the technology could eventually support real-time clinical decision-making in neonatal intensive care units and help reduce the severity of lung disease in vulnerable infants.
AI and neonatal care
A research team from the University of Rochester has developed a new artificial intelligence and machine learning model designed to improve the prediction of bronchopulmonary dysplasia (BPD), a serious lung disease that affects premature newborns. Their findings were published in The Journal of Pediatrics in a study titled “Time-Series Machine Learning for Prediction of Bronchopulmonary Dysplasia.”
The project centres on the use of time-series machine learning, an approach that analyses patterns in data collected over time, allowing researchers to build more dynamic and potentially more accurate disease prediction models.
Understanding bronchopulmonary dysplasia
Bronchopulmonary dysplasia is a chronic lung condition that primarily affects babies born prematurely and with low birth weight. Because their lungs are still underdeveloped, many premature infants require oxygen therapy and mechanical ventilation shortly after birth to survive. However, this early exposure to oxygen and ventilatory support can contribute to lung injury and long-term respiratory complications.
Children who develop BPD may experience ongoing breathing difficulties and other long-term health challenges linked to impaired lung development.
“We take great effort in the neonatal intensive care unit to prevent lung damage,” said Associate Professor Andrew Dylag, MD, from the Department of Pediatrics, Neonatology. “Despite this, premature infants still develop BPD. There are BPD ‘calculators’ on the internet that can predict the severity of lung disease while the baby is still in the hospital, but they use a very limited set of data.”
According to the research team, recently updated versions of these existing calculators demonstrated lower accuracy than earlier models, prompting the group to explore a different strategy.
“We thought that using more detailed data from the University’s electronic health record would improve disease predictions and allow us to pinpoint vulnerable times when we might be able to intervene to prevent lung disease in newborns,” Dylag said.
Funding supports new collaboration
To support the project, the researchers secured a 2023 Digital Health Seedling award from the Clinical and Translational Science Institute (CTSI).
“We hoped that CTSI could help us test the hypothesis that machine learning could improve disease predictions in hospitalized premature newborns,” Dylag said. “The 2023 Digital Health Seedling award was exactly the type of funding we needed to develop new collaborations across the University community and kickstart our team’s academic interactions.”
The funding enabled the formation of a multidisciplinary research team combining expertise from neonatology, engineering, computer science, biostatistics and health informatics.
The collaboration included Jiebo Luo, PhD, Albert Arendt Hopeman Professor of Engineering in the Department of Computer Science, who connected graduate students to the project, as well as Professor Xing Qiu, PhD, from the Department of Biostatistics and Computational Biology.
“The neonatology team brought content and clinical expertise to the work, the computer science team developed and tested the models, and the biostatisticians ensured the rigor and testing of the models and algorithms,” Dylag said.
The project also expanded on an existing partnership with the University of Rochester Clinical and Translational Science Institute’s Informatics and Analytics group, particularly in relation to electronic health record research.
Building a secure AI research environment
Because the project relied on a very large dataset containing sensitive patient information, the research required substantial data security and privacy protections.
“We initially got involved by helping the study team pull clinical data from eRecord,” said Jack Chang, PhD, associate director of Research Informatics. “Recognizing the project involved a very large patient population and massive data including Protected Health Information, we identified a need for a more secure analytical workspace.”
To address these concerns, the Informatics team transitioned the data into the Secure Environment for Research Data Analytics (SERDA), a protected platform designed for high-risk clinical research and advanced analytics.
High-risk healthcare data projects typically involve extensive administrative oversight and cybersecurity requirements to ensure patient privacy and regulatory compliance.
“SERDA removes that obstacle by providing a secure, scalable, and ‘ready-to-go’ environment tailored for advanced analytics and machine learning,” Chang said. “It allows researchers to focus on their science while knowing their data is protected and compliant with all privacy regulations.”
Chang said the Informatics team worked closely with institutional partners to build the infrastructure required for the study.
“Our team—working with our ISD partners—handled the technical heavy lifting: setting up virtual machines, configuring project shares, ensuring secure access with the security team, and customizing the environment with specialized analytical software,” Chang said. “We also provided training and facilitated the numerous exports of analytical outcomes from SERDA for their publication.”
Towards real-time clinical decision support
Researchers believe the improved AI model may help clinicians identify which infants are most likely to benefit from early intervention and more precise treatment strategies.
The long-term goal is to integrate the model into clinical decision support systems capable of updating disease risk predictions in real time as patient conditions evolve.
“We want to build clinical decision support tools to identify how disease predictions change in real time,” Dylag said. “If we validate our algorithm and can present the disease prediction to the clinical team, we can test guideline implementation for how to manage or treat infants that may reduce BPD severity.”
The research team said that continued collaboration between departments, alongside infrastructure support from CTSI and ISD, will be essential as the project progresses into its next phase of development and validation.
Source: University of Rochester Medicine
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