Leveraging a Neural-Symbolic Representation of Biomedical Knowledge to Improve Pediatric Subphenotyping

Tiffany J Callahan, Lawrence E Hunter & Michael G Kahn
Objective: Understanding the molecular drivers of disease is a vital component of personalized medicine. Unfortunately, molecular data are not currently available in most electronic health records (EHRs). To solve this problem we created Med2Mech, a joint learning framework for inferring molecular characterizations of patients from clinical data and publicly available biomedical data. Methods: Med2Mech was evaluated using pediatric EHR data from a subset of rare disease and other similarly medically complex patients. First, patient-level clinical...
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