EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild

TL;DR

EpiGraph is a comprehensive epilepsy knowledge graph that enhances clinical reasoning and benchmarks for evidence-based decision-making using large language models.

Contribution

The paper introduces EpiGraph, a large-scale heterogeneous knowledge graph for epilepsy, and EpiBench, a benchmark for evaluating knowledge-augmented clinical reasoning.

Findings

Integrating EpiGraph improves LLM performance on clinical tasks.

Largest performance gains observed in pharmacogenomic reasoning (+30-41%).

Structured epilepsy knowledge enhances evidence-grounded clinical reasoning.

Abstract

Epilepsy diagnosis and treatment require evidence-intensive reasoning across heterogeneous clinical knowledge, including biosignal patterns, genetic mechanisms, pharmacogenomics, treatment strategies, and patient outcomes. In this work, we present \textsc{EpiGraph}, a large-scale epilepsy knowledge graph and benchmark for evaluating knowledge-augmented clinical reasoning. \textsc{EpiGraph} integrates 48,166 peer-reviewed papers and seven clinical resources into a heterogeneous graph containing 24,324 entities and 32,009 evidence-grounded triplets across five clinical layers. Built upon this graph, \textsc{EpiBench} defines five clinically motivated tasks spanning clinical decision-making, EEG report generation, pharmacogenomic precision medicine, treatment recommendation, and deep research planning. We evaluate six LLMs under both standard and Graph-RAG settings. Results show that integrating \textsc{EpiGraph} consistently improves performance across all tasks, with the largest gains observed in pharmacogenomic reasoning (+30--41\%). Our findings demonstrate that structured epilepsy knowledge substantially enhances evidence-grounded clinical reasoning and provides a practical benchmark framework for evaluating knowledge-augmented LLMs in real-world neurological settings. Our code is available at: https://github.com/LabRAI/EEG-KG.