A machine learning-based framework to identify type 2 diabetes through electronic health records.

ABSTRACT:

Objective

To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate.

Materials and methods

We propose a data informed framework for i

SUBMITTER: Zheng T 

PROVIDER: S-EPMC5144921 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature