HADRec is a Hierarchy-Aware Drug Recommendation framework that fuses molecular knowledge and electronic health records to improve medication recommendation. It uses LLaMA-7B to encode clinical notes, ChemBERTa to encode drug SMILES strings, and a cross‑attention mechanism for multimodal fusion, while a hierarchical predictor and consistency constraint loss enforce adherence to the ATC classification system. Experiments on MIMIC‑III and MIMIC‑IV show state‑of‑the‑art performance, strong generalization, and well‑calibrated predictions, with counterfactual evaluation indicating clinically aligned reasoning.
By Junke Wang, Hongshun Ling, Li Zhang, Jinjing Wu, Tong Shao, Fang Wang, Yuan Gao
arXiv:2510. 21084v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown strong potential for clinical decision support through their advanced language understanding and reasoning capabilities.
By Juntao Li, Haobin Yuan, Ling Luo, Yuanyuan Sun, Jian Wang, Hongfei Lin
arXiv:2608.28624v1 Announce Type: cross
Abstract: Accurate interpretation of single-visit and longitudinal clinical assessments for Parkinson's disease is time-consuming and often depends on speciali...
By Sana Alamgeera, Denise Goberta, Muhammad Irshad, Anne H. H. Ngu
arXiv:2606. 31036v1 Announce Type: new Abstract: Specialist epilepsy expertise is scarce in resource-constrained settings, making LLM-based decision support attractive for frontline clinicians managing longitudinal treatment.
By Shreyas Rajesh, Kartik Sharma, Tonmoy Monsoor, Mehmet Yigit Turali, Richard Idro, Juliana Kayaga, Robert Sebunya, Tracy Tushabe Namata, Jessica Nichole Pasqua, Vwani Roychowdhury, Rajarshi Mazumder
The paper introduces a unified pre‑training framework for medical representations that incorporates hierarchical sub‑token aggregation, partial masking, and cross‑reference mechanisms to better capture the structure of medical codes. The resulting model outperforms existing BERT‑based approaches on pre‑training tasks and downstream clinical predictions, such as dementia onset and hospitalization. An in‑silico drug repositioning study for Alzheimer’s disease demonstrates the framework’s ability to rediscover known drugs and prioritize new hypotheses without external literature, establishing a workflow for hypothesis generation and prioritization based on observational data.
By Yuhei Fujioka, Daitaro Misawa, Shingo Fukuma
arXiv:2508. 14817v2 Announce Type: replace-cross Abstract: Objective: To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).
By Skatje Myers, Dmitriy Dligach, Timothy A. Miller, Samantha Barr, James Landefeld, Yanjun Gao, Matthew Churpek, Anoop Mayampurath, Majid Afshar
arXiv:2509. 08604v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to enhance domain-specific accuracy and safety.
By Anran Li, Lingfei Qian, Mengmeng Du, Yu Yin, Yan Hu, Zihao Sun, Yihang Fu, Hyunjae Kim, Erica Stutz, Xuguang Ai, Qianqian Xie, Rui Zhu, Jimin Huang, Yifan Yang, Siru Liu, Yih-Chung Tham, Lucila Ohno-Machado, Hyunghoon Cho, Zhiyong Lu, Hua Xu, Qingyu Chen
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
The paper introduces CLEAR, an agentic framework designed to improve the reliability of large language models (LLMs) in medical contexts by adjudicating evidence from multiple sources. CLEAR generates candidate answers from three distinct pathways—parametric knowledge, locally curated corpora, and dynamically retrieved evidence—and then uses an aggregation verifier to evaluate agreement and conflict among these sources. An adjudication module decides whether to preserve or revise conclusions, employing override-guard and challenge-audit mechanisms, and initiates targeted follow-up searches when conflicts remain unresolved.
By Shuai Wang, Yize Zhao, Qingyu Chen
arXiv:2608.22062v1 Announce Type: new
Abstract: Longitudinal clinical event-relation verification determines whether a patient record supports a specified relation among two or more clinical events....
By Xingtao Lin, Yubo Feng, Weixin Liu, Hangqi Ren, Junchao Zhou, Caiwan Sun, You Chen
arXiv:2606. 14031v1 Announce Type: new Abstract: Identifying conditions that a certain drug takes therapeutic effect on a target disease is crucial for clinical decision-making support.
By Guanting Luo, Noriki Nishida, Yuji Matsumoto, Yuki Arase
The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.
By Yinzhu Chen, Abdine Maiga, Hossein A. Rahmani, Emine Yilmaz