@inproceedings{wang-etal-2026-saferx,
title = "{S}afe{R}x-Agent: A Knowledge-Grounded Multi-Agent Framework for Safe and Explainable Medication Recommendation",
author = "Wang, Xinyu and
Wu, Hanwei and
Tai, Zhenghan and
Lyu, Sicheng and
Lu, Qincheng and
Zhao, Ziyu and
Chi, Jijun and
Tian, Jingrui and
Chang, Xiao-Wen and
Song, Ziyang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1003/",
pages = "23084--23112",
ISBN = "979-8-89176-459-0",
abstract = "Medication recommendation predicts medications for patient visits, but existing methods still face two key challenges. At the model level, traditional drug recommendation methods only predict structured drug codes with limited evidence grounding, while LLM agents can use richer clinical context but may lack safety verification and traceability. At the task level, existing benchmarks often use broad medication categories, which ignore subgroup-level safety differences and can lead to risk overestimation. We introduce the first fine-grained medication recommendation setting based on fourth-level ATC code generation. We propose \textbf{Safe Prescription Agent (SafeRx-Agent)}, a knowledge-grounded multi-agent framework that uses patient context, external clinical knowledge, and safety verification to recommend traceable medication sets. Experimental results on MIMIC-III and MIMIC-IV datasets show that SafeRx-Agent improves fine-grained medication prediction accuracy while controlling drug interactions, contraindications, and medication set size. Code accompanying this work is available at https://github.com/HWu3150/SafeRx-Agent/tree/main."
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<abstract>Medication recommendation predicts medications for patient visits, but existing methods still face two key challenges. At the model level, traditional drug recommendation methods only predict structured drug codes with limited evidence grounding, while LLM agents can use richer clinical context but may lack safety verification and traceability. At the task level, existing benchmarks often use broad medication categories, which ignore subgroup-level safety differences and can lead to risk overestimation. We introduce the first fine-grained medication recommendation setting based on fourth-level ATC code generation. We propose Safe Prescription Agent (SafeRx-Agent), a knowledge-grounded multi-agent framework that uses patient context, external clinical knowledge, and safety verification to recommend traceable medication sets. Experimental results on MIMIC-III and MIMIC-IV datasets show that SafeRx-Agent improves fine-grained medication prediction accuracy while controlling drug interactions, contraindications, and medication set size. Code accompanying this work is available at https://github.com/HWu3150/SafeRx-Agent/tree/main.</abstract>
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%0 Conference Proceedings
%T SafeRx-Agent: A Knowledge-Grounded Multi-Agent Framework for Safe and Explainable Medication Recommendation
%A Wang, Xinyu
%A Wu, Hanwei
%A Tai, Zhenghan
%A Lyu, Sicheng
%A Lu, Qincheng
%A Zhao, Ziyu
%A Chi, Jijun
%A Tian, Jingrui
%A Chang, Xiao-Wen
%A Song, Ziyang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-saferx
%X Medication recommendation predicts medications for patient visits, but existing methods still face two key challenges. At the model level, traditional drug recommendation methods only predict structured drug codes with limited evidence grounding, while LLM agents can use richer clinical context but may lack safety verification and traceability. At the task level, existing benchmarks often use broad medication categories, which ignore subgroup-level safety differences and can lead to risk overestimation. We introduce the first fine-grained medication recommendation setting based on fourth-level ATC code generation. We propose Safe Prescription Agent (SafeRx-Agent), a knowledge-grounded multi-agent framework that uses patient context, external clinical knowledge, and safety verification to recommend traceable medication sets. Experimental results on MIMIC-III and MIMIC-IV datasets show that SafeRx-Agent improves fine-grained medication prediction accuracy while controlling drug interactions, contraindications, and medication set size. Code accompanying this work is available at https://github.com/HWu3150/SafeRx-Agent/tree/main.
%U https://aclanthology.org/2026.emnlp-main.1003/
%P 23084-23112
Markdown (Informal)
[SafeRx-Agent: A Knowledge-Grounded Multi-Agent Framework for Safe and Explainable Medication Recommendation](https://aclanthology.org/2026.emnlp-main.1003/) (Wang et al., EMNLP 2026)
ACL
- Xinyu Wang, Hanwei Wu, Zhenghan Tai, Sicheng Lyu, Qincheng Lu, Ziyu Zhao, Jijun Chi, Jingrui Tian, Xiao-Wen Chang, and Ziyang Song. 2026. SafeRx-Agent: A Knowledge-Grounded Multi-Agent Framework for Safe and Explainable Medication Recommendation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 23084–23112, Budapest, Hungary. Association for Computational Linguistics.