BEACON-SP：基于本体锚定的图检索增强生成框架用于临床自杀风险评估

中文摘要 AI 辅助

我们提出了BEACON-SP，一个面向临床医生的决策支持框架，基于本体锚定的图检索增强生成（GraphRAG），适用于行为健康环境（如自杀预防），在这些环境中，有效的评估需要整合异质的临床、行为、社会和时间证据。BEACON-SP将患者知识图谱与本体引导的检索相结合，支持跨诊断、药物、风险与保护因素、生活事件和时间关系的多跳推理。该框架由一个全面的自杀预防本体支撑，该本体将三步理论、整合动机-意志模型和自杀健康社会决定因素本体整合为患者风险因素的统一表示。我们构建了基于本体的患者知识图谱，并评估了BEACON-SP在面向临床医生的问题回答中的表现。与基于向量的检索增强生成（RAG）基线相比，在涵盖15个临床类别和100名患者的1,500个查询基准上，BEACON-SP在修正的比较评估协议下提高了完整性、临床相关性和证据锚定性，并在事实准确性上略有提升。在配对的标准级比较中，GraphRAG在76.4%的情况下被优先选择。这些结果证明了本体引导的GraphRAG在提供结构化、情境化的患者证据以支持临床决策方面的潜力。

英文摘要

We present BEACON-SP, an ontology-grounded Graph Retrieval-Augmented Generation (GraphRAG) framework for clinician-facing decision support in behavioral health settings such as suicide prevention, where effective assessment requires integrating heterogeneous clinical, behavioral, social, and temporal evidence. BEACON-SP combines patient knowledge graphs with ontology-guided retrieval to support multi-hop reasoning across diagnoses, medications, risk and protective factors, life events, and temporal relationships. The framework is enabled by a comprehensive suicide prevention ontology that integrates the Three-Step Theory, the Integrated Motivational-Volitional Model, and the Suicide Social Determinants of Health Ontology into a unified representation of patient risk factors. We construct ontology-grounded patient knowledge graphs and evaluate BEACON-SP for clinician-facing question answering. Compared with a vector-based retrieval-augmented generation (RAG) baseline on a 1,500-query benchmark spanning 15 clinical categories and 100 patients, BEACON-SP improves completeness, clinical relevance, and evidence grounding under a corrected comparative evaluation protocol, with a small gain on factual accuracy. In paired criterion-level comparisons, GraphRAG is preferred in 76.4% of cases. These results demonstrate the potential of ontology-guided GraphRAG to provide structured, contextualized patient evidence for clinical decision support.

发表机构

• Nexcepta
• United States Military Academy（美国军事学院）

机构由 AI 辅助整理，请以论文原文为准。