Prediction of Diabetic Retinopathy Using Longitudinal Electronic Health Records

Diabetic retinopathy (DR) is a microvascular complication of diabetes and is a leading cause of vision loss and blindness. Screening and early detection of DR is critical but current screening methods rely on eye care experts and expensive medical equipment, which are not available in medically underserved communities. The non-image-based, machine-learning approach in this study aims to detect DR in the early stage using demographics, comorbidities, and routine lab results data, which are widely available for diabetic patients. We develop different temporal deep learning models to analyze a real-world, large-scale dataset and compare performances of these models. Experimental results show that temporal models outperform baseline random forest models in metrics of AUPRC and recall.