HEM-Med introduces a hierarchical agentic memory framework for safe multi‑center medication recommendation, addressing two key gaps in existing methods. It disentangles cross‑hospital clinical experience from hospital‑specific prescribing patterns via a hierarchical memory module, and incorporates a safety‑aware medication set generation module that considers drug‑drug interactions and local formulary constraints. Experiments on a real‑world multi‑center eICU dataset demonstrate that HEM‑Med outperforms representative transfer learning, LLM‑based, and MR baselines in both recommendation accuracy and safety.
By Min Xiao, Jin Li, Shoujin Wang, Yeguang Zhen, Yishuo Li, Wenpeng Lu
Medication recommendation (MR) is critical for clinical decision support. While existing MR methods are mostly designed for single-center learning, where only one independent hospital is involved, mul...
arXiv:2610.14474v1 Announce Type: cross
Abstract: Medication recommendations seek to deliver personalized drug combinations based on patients' clinical information, where ensuring transparent decisio...
By Cong Wang, Jin Li, Shoujin Wang, Yishuo Li, Huilin Gu, Wenpeng Lu
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
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
The paper introduces DeToxR, a reinforcement‑learning‑enhanced large language model designed to support decision making in acute toxicology cases. It fuses unstructured narratives from paramedics and patients with structured vital‑sign data to predict co‑ingested substances across 14 classes. In preliminary validation, DeToxR outperforms baseline models, achieving higher micro‑F1 and recall scores for poison identification.
By Nico Oberl\"ander, David Bani-Harouni, Tobias Zellner, Nassir Navab, Florian Eyer, Matthias Keicher