Algorithmic identification of atypical diabetes in electronic health record (EHR) systems.

Sara J CromerVictoria ChenChristopher HanWilliam MarshallShekina EmongoEvelyn GreauxTim MajarianJose C FlorezJosep MercaderMiriam S Udler

Published in: PloS one (2022)

Our EHR-based algorithms followed by manual chart review identified collectively 16 individuals with AD, representing 0.22% of biobank enrollees with T2D. With a maximum yield of 48% cases after manual chart review, our algorithms have the potential to drastically improve efficiency of AD identification. Recognizing patients with AD may inform on the heterogeneity of T2D and facilitate enrollment in studies like the Rare and Atypical Diabetes Network (RADIANT).

Keyphrases

electronic health record
machine learning
type diabetes
clinical decision support
cardiovascular disease
glycemic control
deep learning
adverse drug
single cell
bioinformatics analysis
health insurance
metabolic syndrome
risk assessment
skeletal muscle