Khaled El Emam

Abstract Recurrent acute care visits are a common yet preventable outcome for many children with asthma. Machine learning (ML) applied to electronic medical records (EMR) may help identify children at high risk and enable targeted referral to preventative care. We developed ML models to predict repeat asthma-related emergency department (ED) visits or hospital admissions within one year among children with a prior asthma ED visit at a tertiary children’s hospital.

This study found that machine learning more accurately predicted which youth with eating disorders were likely to develop a complex clinical course based on clinical data from their first treatment episode, compared to traditional methods. These findings highlight the potential of machine learning to support personalized medicine initiatives and move beyond the standardized treatment approaches commonly used in the eating disorder field.

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