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[2602.12828] GRAIL: Geometry-Aware Retrieval-Augmented Inference with LLMs over Hyperbolic Representations of Patient Trajectories
Summary
The GRAIL framework enhances next-visit event prediction in healthcare by utilizing geometry-aware retrieval and hyperbolic representations of patient trajectories, improving accuracy in forecasting clinical events.
Why It Matters
This research addresses the challenges of predicting clinical events from electronic health records, a critical need in healthcare analytics. By improving the accuracy of predictions, GRAIL can potentially lead to better patient outcomes and more efficient healthcare delivery, making it a significant advancement in the field of machine learning and AI applications in healthcare.
Key Takeaways
- GRAIL models patient trajectories using structured geometric representations.
- The framework improves next-visit event predictions by integrating hierarchical and temporal data.
- GRAIL enhances prediction accuracy through a combination of deterministic coding and data-driven associations.
- It employs LLMs for refined ranking of clinically plausible future events.
- Experiments show GRAIL consistently outperforms existing methods in multi-type predictions.
Computer Science > Machine Learning arXiv:2602.12828 (cs) [Submitted on 13 Feb 2026] Title:GRAIL: Geometry-Aware Retrieval-Augmented Inference with LLMs over Hyperbolic Representations of Patient Trajectories Authors:Zhan Qu, Michael Färber View a PDF of the paper titled GRAIL: Geometry-Aware Retrieval-Augmented Inference with LLMs over Hyperbolic Representations of Patient Trajectories, by Zhan Qu and Michael F\"arber View PDF HTML (experimental) Abstract:Predicting future clinical events from longitudinal electronic health records (EHRs) is challenging due to sparse multi-type clinical events, hierarchical medical vocabularies, and the tendency of large language models (LLMs) to hallucinate when reasoning over long structured histories. We study next-visit event prediction, which aims to forecast a patient's upcoming clinical events based on prior visits. We propose GRAIL, a framework that models longitudinal EHRs using structured geometric representations and structure-aware retrieval. GRAIL constructs a unified clinical graph by combining deterministic coding-system hierarchies with data-driven temporal associations across event types, embeds this graph in hyperbolic space, and summarizes each visit as a probabilistic Central Event that denoises sparse observations. At inference time, GRAIL retrieves a structured set of clinically plausible future events aligned with hierarchical and temporal progression, and optionally refines their ranking using an LLM as a constrain...
