Validating a Deep Learning Algorithm to Identify Patients with Glaucoma using Systemic Electronic Health Records
We evaluated whether a glaucoma risk assessment (GRA) model trained on All of Us national data can identify patients at high probability of glaucoma using only systemic electronic health records (EHR) at an independent institution. In this cross-sectional study, 20,636 Stanford patients seen from November 2013 to January 2024 were included (15% with glaucoma). A pretrained GRA model was fine-tuned on the Stanford cohort and tested on a held-out set using demographics, systemic diagnoses, medications, laboratory results, and physical examination measurements as inputs. The best model achieved AUROC 0.883 and PPV 0.657. Calibration was consistent with clinical risk: the highest prediction d...
| Authors | John Xiang, Rohith Ravindranath, Sophia Y. Wang |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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