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BEACON-SP is an ontology‑grounded Graph Retrieval‑Augmented Generation framework designed to aid clinicians in suicide risk assessment by integrating diverse clinical, behavioral, social, and temporal data. It constructs patient knowledge graphs guided by a unified suicide prevention ontology that merges the Three‑Step Theory, the Integrated Motivational‑Volitional Model, and the Suicide Social Determinants of Health Ontology. In a benchmark of 1,500 queries across 15 clinical categories, BEACON‑SP outperformed a vector‑based RAG baseline in completeness, clinical relevance, and evidence grounding, with 76.4% of paired comparisons favoring the GraphRAG approach.
The paper introduces a framework for explainable suicide risk assessment from social networking service posts, comprising Risk Assessment, Evidence Grounding, and Factor Identification. Risk Assessment uses length-based routing to handle varying post lengths, while Evidence Grounding highlights supporting phrases and enforces consistency with risk predictions. Factor Identification employs two verifiers—Taxonomy Verifier and Evidence-Aware Verifier—to predict psychosocial factors, achieving weighted F1 scores of 0.8088, 0.7605, and 0.5562 for the three tasks respectively.
arXiv:2607. 18270v1 Announce Type: new Abstract: While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge.
BiGraph-Diffuse is a large‑scale diffusion language model designed for mental health counseling, addressing two key limitations of existing AI dialogue systems: the lack of bidirectional understanding for progressive disclosure and the inadequate use of relational clinical knowledge. It pairs this diffusion model with BiGraph‑RAG, a graph‑structured retrieval approach that uses lightweight entity extraction and semantic linking to preserve inferential pathways from symptoms to underlying causes without incurring LLM token costs during indexing. Experiments and theoretical analysis demonstrate the effectiveness of this mutually reinforcing architecture.
arXiv:2606. 19637v1 Announce Type: cross Abstract: Clinical NLP increasingly relies on electronic health record (EHR) data to detect suicidal behaviors, treating clinical documentation as more reliable ground truth than social media.
arXiv:2606. 29876v1 Announce Type: cross Abstract: Modern large language models (LLMs) reach 60-70% diagnostic accuracy on complex clinical case benchmarks, but accuracy alone cannot distinguish stable clinically-grounded reasoning from pattern matching.