Improving the Identification of Diabetic Retinopathy and Related Conditions in the Electronic Health Record Using Natural Language Processing Methods.

ABSTRACT:

Purpose

To compare the performance of 3 phenotyping methods in identifying diabetic retinopathy (DR) and related clinical conditions.

Design

Three phenotyping methods were used to identify clinical conditions including unspecified DR, nonproliferative DR (NPDR) (mild, moderate, severe), consolidated NPDR (unspecified DR or any NPDR), proliferative DR, diabetic macular edema (DME), vitreous hemorrhage, retinal detachment (RD) (tractional RD or combined tractional and rhegmatogenous RD), and neovascular glaucoma (NVG). The first method used only International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes (ICD-10 Lookup System). The next 2 methods used a Bidirectional Encoder Representations from Transformers with a dense Multilayer Perceptron output

SUBMITTER: Harrigian K 

PROVIDER: S-EPMC11382176 | biostudies-literature | 2024 Nov-Dec

REPOSITORIES: biostudies-literature