An effort to develop a natural language processing (NLP) algorithm to identify patients with active uveitic macular edema (UME) from electronic health records (EHR) data in the American Academy of Ophthalmology IRIS Registry (Intelligent Research in Sight) resulted in an algorithm that identified 3 times more patients with active UME compared to only using ICD-10 codes. The findings, which were presented at ARVO 2024, suggest that the new algorithm can provide an enhanced solution to conducting real-world evidence studies in the UME patient population.

The proposed algorithm achieved an accuracy, sensitivity, and specificity of 0.83, 0.95, and 0.73, respectively. Out of 231,543 patients with UME keywords in their clinical records, 129,316 patients were confirmed with active UME at the encounter level by the proposed NLP algorithm. Alternatively, 40,277 patients were identified as having active UME diagnosis using the ICD-10 codes alone.