Using machine learning to identify pediatric ophthalmologists.
Publication/Presentation Date
2-1-2025
Abstract
This cross-sectional study used data from the American Academy of Ophthalmology IRIS Registry (Intelligent Research in Sight) and machine learning algorithms to identify pediatric ophthalmologists based on physician coding patterns. A random forest model achieved an area under the receiver operating characteristic curve of 0.98, sensitivity of 0.98, and specificity of 0.88 when classifying pediatric eye specialists in the test validation cohort. Algorithm-based approaches to identify pediatric ophthalmologists using procedure codes may offer new avenues to determine the scope, scale, and trajectory of pediatric eye care delivery.
Volume
29
Issue
1
First Page
104052
Last Page
104052
ISSN
1528-3933
Published In/Presented At
Oke, I., Elze, T., Miller, J. W., Lorch, A. C., Ong, M. S., Wu, A. C., Hunter, D. G., & IRIS® Registry Analytic Center Consortium (2025). Using machine learning to identify pediatric ophthalmologists. Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus, 29(1), 104052. https://doi.org/10.1016/j.jaapos.2024.104052
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
39566680
Department(s)
Administration and Leadership
Document Type
Article