Objective Accurate and rapid phenotyping is a prerequisite to leveraging electronic health
records for biomedical research. While early phenotyping relied on rule-based algorithms …

Aims To systematically evaluate the diagnostic value of an artificial intelligence (AI)
algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the …

OBJECTIVE Although diabetic retinopathy is a leading cause of blindness worldwide,
diabetes-related blindness can be prevented through effective screening, detection, and …

The adoption of electronic health records in hospitals has ensured the availability of large
datasets that can be used to predict medical complications. The trajectories of patients in …

Diabetic eye disease is a major cause of blindness worldwide. The ability to monitor relevant
clinical trajectories and detect lapses in care is critical to managing the disease and …

Background To apply machine learning (ML) algorithms to perform multiclass diabetic
retinopathy (DR) classification using both clinical data and optical coherence tomography …

Purpose To conduct a systematic review to assess drug exposure handling in diabetic
retinopathy (DR) risk prediction models, a network-meta-analysis to identify drugs …

Background Diabetic retinopathy (DR) is one of the most common complications in type 2
diabetes (T2D) with an estimated prevalence of 22%. Predictive modelling has largely been …

Purpose: To assess the role of artificial intelligence (AI) based automated software for
detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography …

Objective The paper aims to address the problem of massive unlabeled patients in
electronic health records (EHR) who potentially have undiagnosed diabetic retinopathy …