Using Machine Learning to Identify Ophthalmology Subspecialty Care and Advance Workforce Research with the IRIS® Registry (Intelligent Research in Sight).

ABSTRACT:

Purpose

To develop machine-learning models to identify ophthalmology subspecialists using deidentified patient data from a large database.

Design

Cross-sectional.

Participants

All ophthalmologists participating in the American Academy of Ophthalmology's IRIS® Registry (Intelligent Research in Sight) from 2013 to 2023 were classified under one of the following general or subspecialty categories: comprehensive, cataract, cornea, glaucoma, retina, oculofacial, pediatric, or neuro-ophthalmology.

Methods

We collected the diagnosis, procedure, and prescription codes linked to each ophthalmologist. We performed binary subspecialty classification using random forest models with fivefold cross validation and multispecialty classification using 4 approaches (diagnosis on

SUBMITTER: Jeon JH 

PROVIDER: S-EPMC12302636 | biostudies-literature | 2025 Nov-Dec

REPOSITORIES: biostudies-literature