Machine learning improves identification of asthma risk in children
A new machine learning tool called the Passive Digital Marker analyzes existing electronic health records to help pediatricians identify children at risk for persistent asthma. The tool provides a risk assessment without requiring additional testing or patient questionnaires.
Why it matters
By leveraging existing data, this tool offers a non-invasive and efficient way to improve early diagnosis and management of chronic childhood respiratory conditions.
edited by Gaby Clark , reviewed by Andrew Zinin
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Add as preferred source Credit: Pixabay/CC0 Public Domain 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.
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