Abstract:Effectively stratifying patient risk in chronic diseases like glaucoma is a major clinical challenge. Clinicians need tools to identify patients at high risk of progression from sparse and irregularly-sampled electronic health records (EHRs). We propose a novel deep kernel learning (DKL) architecture that leverages a Gaussian Process (GP) backend. The GP's kernel is defined by a transformer-based feature extractor applied to clinical-BERT embeddings to model glaucoma patient trajectories from multimodal EHR data. Our method successfully identifies three clinically distinct patient subgroups. Crucially, the model learns to decouple disease progression from current severity, identifying a high-risk group with a worsening trajectory despite having better average visual acuity than a second, stably poor group. This reveals that the model learns to identify progression risk rather than just the current disease state. This ability to stratify patients based on their risk trajectory progression offers a powerful tool for clinical decision support, enabling targeted interventions for high-risk individuals and improving the management of glaucoma care.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.00708 [cs.LG] |
| (or arXiv:2605.00708v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.00708 arXiv-issued DOI via DataCite (pending registration) |
From: Bruce Rushing [view email]
[v1]
Fri, 1 May 2026 14:51:55 UTC (9,279 KB)
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