Comparison of Diagnosis Codes to Clinical Notes in Classifying Patients with Diabetic Retinopathy.

ABSTRACT:

Purpose

Electronic health records (EHRs) contain a vast amount of clinical data. Improved automated classification approaches have the potential to accurately and efficiently identify patient cohorts for research. We evaluated if a rule-based natural language processing (NLP) algorithm using clinical notes performed better for classifying proliferative diabetic retinopathy (PDR) and nonproliferative diabetic retinopathy (NPDR) severity compared with International Classification of Diseases, ninth edition (ICD-9) or 10th edition (ICD-10) codes.

Design

Cross-sectional study.

Subjects

Deidentified EHR data from an academic medical center identified 2366 patients aged ≥18 years, with diabetes mellitus, diabetic retinopathy (DR), and available clinical notes.

Methods

SUBMITTER: Yonamine S 

PROVIDER: S-EPMC11382306 | biostudies-literature | 2024 Nov-Dec

REPOSITORIES: biostudies-literature