HEM-Med: A Hierarchical Agentic Memory Framework for Safe Multi-Center Medication Recommendation

Min Xiao; Jin Li; Shoujin Wang; Yeguang Zhen; Yishuo Li; Wenpeng Lu

cs.AIOct 14, 2026

HEM-Med: A Hierarchical Agentic Memory Framework for Safe Multi-Center Medication Recommendation

Authors: Min Xiao, Jin Li, Shoujin Wang, Yeguang Zhen, Yishuo Li, Wenpeng Lu

Organizations: Key Laboratory of Computing Power Network and Information Security, Ministry of Education; Shandong Computer Science Center (National Supercomputer Center in Jinan); Qilu University of Technology (Shandong Academy of Sciences); Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing; Shandong Fundamental Research Center for Computer Science, Jinan, Shandong, China · University of Technology Sydney

Abstract

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, multi-center MR with hospitals of different scales has become increasingly demanding in practice. In multi center settings, abundant data from large medical centers can improve recommendation performance for low-resource centers. Despite a few preliminary attempts on this task, two critical gaps remain. Gap 1: Existing multi-center methods fail to disentangle transferable cross-hospital clinical experience from hospital-specific prescribing patterns, limiting clinical feasibility and physician acceptance. Gap 2: They commonly overlook multi label safety constraints of MR, including drug-drug interaction risks and local formulary feasibility, which are essential for safe real-world deployment. To address these gaps, we propose HEM-Med, a novel hierarchical agentic memory framework for multi-center MR. To address Gap 1, we design a disentangled hierarchical memory module that builds patient-level medication context attribution records and disentangles them into cross hospital experience memory and hospital-specific residual memory. To address Gap 2, we introduce a safety-aware medication set generation module, which integrates dual-level experience retrieval with safety-aware set decoding to produce clinically relevant, safer, and locally adaptive medication recommendations. Experiments on a real-world multi-center eICU dataset show that HEM-Med outperforms representative transfer learning, LLM-based, and MR baselines in terms of both recommendation accuracy and safety.

Figures & tables

Fig. 1 : Motivating example of multi-center medication recommendation. Heterogeneous hospital prescribing patterns may cause negative transfer, and ignoring local feasibility and drug safety may lead to unsafe medication combinations.

Fig. 2 : Overall agentic framework of HEM-Med. HEM-Med retrieves disentangled cross-hospital and hospital-specific experience, and generates medication sets through calibrated prediction and DDI-aware decoding.

Item	Number
# of records / hospitals	90,545 / 149
Avg. / max / min # of records per hospital	607.68 / 3,315 / 1
Diag. / proc. / med. vocabulary size	2,958 / 1,852 / 161
Lab vocabulary size	12
Avg. / max / min # of diagnoses per record	4.04 / 91 / 1
Avg. / max / min # of procedures per record	6.60 / 102 / 1

TABLE I : Statistics of the processed eICU dataset.

Model	PRAUC $\uparrow$	F1 $\uparrow$	Jaccard $\uparrow$	DDI Rate $\downarrow$	SafeScore $\uparrow$
EMCDR	$0.3421\pm 0.0043$	$0.3716\pm 0.0086$	$0.2647\pm 0.0095$	$0.1321\pm 0.0062$	$0.2831\pm 0.0044$
DDTCDR	$0.3598\pm 0.0136$	$0.3763\pm 0.0019$	$0.2665\pm 0.0102$	$0.1231\pm 0.0049$	$0.2931\pm 0.0053$
TEMPT	$0.3430\pm 0.0065$	$0.3335\pm 0.0054$	$0.2284\pm 0.0075$	$0.1204\pm 0.0038$	$0.2653\pm 0.0035$
Reason4Rec	$0.3697\pm 0.0083$	$0.3893\pm 0.0022$	$0.2783\pm 0.0036$	$\mathbf{0.0814\pm 0.0064}$	$0.3176\pm 0.0036$
EXP3RT	$0.3732\pm 0.0148$	$0.3944\pm 0.0026$	$0.2819\pm 0.0028$	$0.0927\pm 0.0014$	$0.3174\pm 0.0046$
SafeDrug	$\underline{0.3926\pm 0.0038}$	$\underline{0.4082\pm 0.0061}$	$\underline{0.2939\pm 0.0049}$	$0.0862\pm 0.0064$	$\underline{0.3334\pm 0.0035}$

TABLE II : Main experimental results on the eICU dataset.

Variant	PRAUC $\uparrow$	F1 $\uparrow$	Jaccard $\uparrow$	DDI Rate $\downarrow$	SafeScore $\uparrow$
HEM-Med w/o MD	$0.3723\pm 0.0145$	$\underline{0.4221\pm 0.0019}$	$\underline{0.2938\pm 0.0092}$	$0.0993\pm 0.0187$	$\underline{0.3267\pm 0.0085}$
HEM-Med w/o DDI-Dec	$0.3631\pm 0.0136$	$0.4175\pm 0.0021$	$0.2840\pm 0.0102$	$0.1254\pm 0.0074$	$0.3104\pm 0.0056$
HEM-Med w/o RERANKER	$\underline{0.4025\pm 0.0049}$	$0.3235\pm 0.0078$	$0.2111\pm 0.0053$	$0.0953\pm 0.0049$	$0.2826\pm 0.0036$
HEM-Med w/o WAM	$0.3677\pm 0.0047$	$0.4117\pm 0.0091$	$0.2824\pm 0.0062$	$\underline{0.0886\pm 0.0037}$	$0.3226\pm 0.0039$
HEM-Med	$\mathbf{0.4161\pm 0.0086}$	$\mathbf{0.4532\pm 0.0062}$	$\mathbf{0.3246\pm 0.0074}$	$\mathbf{0.0831\pm 0.0035}$	$\mathbf{0.3649\pm 0.0042}$

TABLE III : Ablation study results on the eICU dataset.

Panel of Figure 6 — Fig. 3 : Parameter analysis of weak experience weight and candidate depth.

Method	Case A: H452	Case B: H444
Ground Truth	Dex; Fur; NaCl; KCl; MgSO 4	Enox; Hyd; Lor; NaCl; KCl
DDTCDR	3/5 NaCl ; MgSO 4 ; CaGlu ; KCl ; APAP Miss: Dex; Fur	3/5 KCl ; Ond ; APAP ; NaCl ; Lor Miss: Enox; Hyd
SafeDrug	4/5 NaCl ; CaGlu ; MgSO 4 ; KCl ; Dex Miss: Fur	4/5 KCl ; Lor ; Eso ; NaCl ; Enox Miss: Hyd
TEMPT	3/5 NaCl ; Lor ; Dex ; KCl ; Glu Miss: Fur; MgSO 4	4/5 Enox ; Lor ; NaCl ; KCl ; Eso Miss: Hyd
RES-MR	4/5 NaCl ; KCl ; MgSO 4 ; CaGlu ; Dex Miss: Fur	4/5 KCl ; Lor ; NaCl ; Eso ; Enox Miss: Hyd
HEM-Med	5/5 NaCl ; KCl ; MgSO 4 ; Dex ; Fur Miss: None	5/5 Lor ; Enox ; KCl ; Hyd ; NaCl Miss: None

TABLE IV : Case study comparison between two clinically similar ICU stays from different hospitals.

Medication Recommendation

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May 27, 2026

SafeRx-Agent: A Knowledge-Grounded Multi-Agent Framework for Safe and Explainable Medication Recommendation

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.

Xinyu Wang, Hanwei Wu, Zhenghan Tai +7

McGill University · McMaster University · University of Toronto +1

Medication Recommendation Multi-Agent LLM Systems Drug-Drug Interaction Prediction Healthcare Clinical Decision Support LLM Agent Safety

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Mar 19, 2026

HypeMed: Enhancing Medication Recommendations with Hypergraph-Based Patient Relationships

Medication recommendations aim to generate safe and effective medication sets from health records. However, accurately recommending medications hinges on inferring a patient's latent clinical condition from sparse and noisy observations, which requires both (i) preserving the visit-level combinatorial semantics of co-occurring entities and (ii) leveraging informative historical references through effective, visit-conditioned retrieval. Most existing methods fall short in one of both aspects: graph-based modeling often fragments higher-order intra-visit patterns into pairwise relations, while inter-visit augmentation methods commonly exhibit an imbalance between learning a globally stable representation space and performing dynamic retrieval within it. To address these limitations, this paper proposes HypeMed, a two-stage hypergraph-based framework unifying intra-visit coherence modeling and inter-visit augmentation. HypeMed consists of two core modules: MedRep for representation pre-training, and SimMR for similarity-enhanced recommendation. In the first stage, MedRep encodes clinical visits as hyperedges via knowledge-aware contrastive pre-training, creating a globally consistent, retrieval-friendly embedding space. In the second stage, SimMR performs dynamic retrieval within this space, fusing retrieved references with the patient's longitudinal data to refine medication prediction. Evaluation on real-world benchmarks shows that HypeMed outperforms state-of-the-art baselines in both recommendation precision and DDI reduction, simultaneously enhancing the effectiveness and safety of clinical decision support.

Xiangxu Zhang, Xiao Zhou, Hongteng Xu +1

Gaoling School of Artificial Intelligence, Renmin University of China, China · Beijing Key Laboratory of Research on Large Models and Intelligent Governance · Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE +1

Medication Recommendation Recommender Systems Clinical Decision Support Longitudinal EHR Modeling Hypergraph Learning

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Mar 21, 2026

GraphDiffMed: Knowledge-Constrained Differential Attention with Pharmacological Graph Priors for Medication Recommendation

Recommending safe and effective medication combinations from electronic health records (EHRs) is a core clinical AI problem, yet it remains difficult because patient trajectories are long, noisy, and clinically heterogeneous. Existing methods typically excel at either temporal modeling across visits or pharmacological knowledge integration (e.g., drug-drug interactions, DDIs), but rarely achieve both while robustly suppressing noise. We present GraphDiffMed, a knowledge-constrained medication recommendation framework built on dual-scale Differential Attention v2. Differential attention is applied at both intra-visit and inter-visit levels to filter spurious signals within encounters and across longitudinal history, while pharmacological constraints are incorporated during learning. Experiments on MIMIC-III and ablation studies show that this design consistently improves recommendation quality and ranking over strong baselines while achieving a more favorable safety performance balance. We further find that the strongest-performing configuration uses only demographic auxiliary features under our experimental setting. Overall, GraphDiffMed demonstrates that combining noise-aware attention with pharmacological constraints yields more reliable and clinically meaningful medication recommendation. We open-source our code at https://github.com/saxenakrati09/GraphDiffMed.

Krati Saxena, Tomohiro Shibata

Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan

Medication Recommendation Differential Attention Clinical Decision Support Longitudinal EHR Modeling Electronic Health Records

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Abstract

Medication Recommendation

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