[
    {
        "id": "osp-15454",
        "type": "article-journal",
        "title": "BEACON-SP: Ontology-Grounded GraphRAG Framework for Clinical Suicide Risk Assessment",
        "author": [
            {
                "family": "Davaslioglu",
                "given": "Kemal"
            },
            {
                "family": "Conger",
                "given": "Nathan"
            },
            {
                "family": "Kompella",
                "given": "Sastry"
            },
            {
                "family": "Sagduyu",
                "given": "Yalin E."
            },
            {
                "family": "Bastian",
                "given": "Nathaniel D."
            }
        ],
        "URL": "https://omanscience.com/ar/articles/beacon-sp-ontology-grounded-graphrag-framework-for-clinical-suicide-risk-assessment",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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."
    }
]