Significant EHR Feature-Driven T2D Inference: Predictive Machine Learning and Networks.

ABSTRACT: Background: Electronic health records (EHR) play an important role for the redefinition of phenotypes in view of the wealth and heterogeneity of information now available from disparate data sources. A recent cross-sectional retrospective study has described the potential of EHR toward type 2 diabetes mellitus (T2D) screening when ad hoc models are used. About 10,000 US patients have been analyzed through a variety of inference techniques applied to all records with a variable degree of completeness. The analyses conducted in the reference study have indicated that EHR phenotypes significantly improved T2D detection. Methods: With these US patients and the T2D data evidenced in the above study, we propose an integrative inference approach that leverages the prediction

SUBMITTER: Preo N 

PROVIDER: S-EPMC7931876 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature