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.
By Kemal Davaslioglu, Nathan Conger, Sastry Kompella, Yalin E. Sagduyu, Nathaniel D. Bastian
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
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
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: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.
By Priyanshi Garg, Ishita Rao, Jieqiong Ding, Amandalynne Paullada
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.
By Nisarg A. Patel (University of California, San Francisco)
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
arXiv:2606. 15155v1 Announce Type: new Abstract: Knowledge graphs (KGs) have emerged as a promising solution for integrating and reasoning over complex biomedical and clinical data in healthcare.
By Haniye Sherafatmandjoo, Mohammad Akbari, Zahed Rahmati
arXiv:2607. 22592v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (GraphRAG) grounds answers in structured knowledge, but current systems extract entities and relationships exhaustively, producing graphs whose size and construction cost scale with corpus length rather than with the reasoning a query requires.
By Marc Saouda (Boston Consulting Group), Rajprakash Bale (Boston Consulting Group), Eren Aldis (Boston Consulting Group), Cloves Almeida (Boston Consulting Group)
arXiv:2510.03536v3 Announce Type: replace-cross
Abstract: Multi-turn medical question answering (QA) aims to model realistic clinical diagnosis, where a doctor gathers patient information across mult...
By Zhaohan Meng, Zaiqiao Meng, Siwei Liu, Hao Xu, Ke Yuan, Iadh Ounis
arXiv:2606. 26205v1 Announce Type: new Abstract: Patients increasingly seek medication information online, yet safety knowledge for psychiatric drugs is split between regulatory adverse-event records, which are authoritative but abstract, and patient narratives, which are experience-near but unvalidated.
By Huizi Yu, Jian Liu, Wenkong Wang, Lingyao Li, Jiayan Zhou, Zhaoqian Xue, Xiang Li, Xinxin Lin, Zhiying Liang, Zhuoru Wu, Siyuan Ma, Xin Ma, Lizhou Fan