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, whe...
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.
By Tianle Hu, Chen Peng, Yi-Hsin Tsai, Takshing Andy Tung, Bingyang Sun, Yenjou Wang
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.
By Yuxiang Cheng, Quanwei Tang, Lvhui Lu, Dong Zhang, Shoushan Li, Erik Cambria
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.
By Kyunghoon Jeon, Youmin Ko, Woohwan Jung, Hyunjoon Kim
arXiv:2608. 15382v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather than reasoning about interventions, mechanisms, harms, evidence, and uncertainty.
By Ummara Mumtaz, Aimen Noor, Awais Ahmed
arXiv:2608.15382v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy r...
By Ummara Mumtaz, Aimen Noor, Awais Ahmed