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
arXiv:2607. 06605v1 Announce Type: new Abstract: Conformal prediction is being adopted in drug discovery to put an honest number on model reliability: pick an error rate alpha, and the method returns prediction sets containing the true label with probability at least 1 - alpha.
By Muhammadjon Tursunbadalov (School of Science and Technology, Champions College Prep, United States), Mustafojon Tursunbadalov (School of Science and Technology, Champions College Prep, United States)
arXiv:2609.22959v1 Announce Type: cross
Abstract: The medications prescribed to a patient often change during a hospital admission as clinicians start, stop, or continue therapies. We study whether m...
By Regan Mahat, Mansu Kim
The paper introduces SBERT2S1, a method that transforms Sentence-Transformers encoders into typed decision models for biomedical text, and presents BIODECIDE, a suite for evaluating such models, along with MEDLINE‑S1, a large training set of 243k decisions derived from NLM indexing. Experiments show that retrieval‑trained encoders improve zero‑shot matching of content‑bearing options, and that a prior‑fused residual (PFR) head benefits most from retrieval pre‑training, while a cross‑head (C) head generally outperforms PFR across all objectives. The authors also release code, data, and a model, and discuss calibration and reward‑normalisation effects in their RLCD recipe.
By Pritam Deka
arXiv:2610.19762v1 Announce Type: new
Abstract: Medical prescribing errors for multimorbid patients, who require complex drug regimens, are a major patient safety concern. These errors often stem fro...
By Ziyang Ling, Shuyu Lu, Ziyi Chen
arXiv:2606. 30887v1 Announce Type: cross Abstract: Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive metric.
By Mizanur Rahman, Abeer Badawi, Elahe Rahimi, Laleh Seyyed-Kalantari, Frank Rudzicz, Enamul Hoque, Elham Dolatabadi
arXiv:2608. 03028v1 Announce Type: new Abstract: Applying a valid medication-safety rule when its patient-specific conditions are not met can produce an incorrect decision.
By Zhitian Hou, Yuhang Liu, Pengkai Wang, Zeyu Liu, Guanghao Zhu, Zheng Liu, Shuo Cai, Congkai Xie, Zhijie Sang, Kun Zeng, Hongxia Yang