Detecting diabetic retinopathy through machine learning on electronic health record data from an urban, safety net healthcare system.

ABSTRACT:

Objective

Clinical guidelines recommend annual eye examinations to detect diabetic retinopathy (DR) in patients with diabetes. However, timely DR detection remains a problem in medically underserved and under-resourced settings in the United States. Machine learning that identifies patients with latent/undiagnosed DR could help to address this problem.

Materials and methods

Using electronic health record data from 40 631 unique diabetic patients seen at Los Angeles County Department of Health Services healthcare facilities between January 1, 2015 and December 31, 2017, we compared ten machine learning environments, including five classifier models, for assessing the presence or absence of DR. We also used data from a distinct set of 9300 diabetic patients seen between Januar

SUBMITTER: Ogunyemi OI 

PROVIDER: S-EPMC8374369 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature