# Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation

arXiv: https://arxiv.org/abs/2504.18096 · HTML: https://mlsanity.com/paper/2504.18096/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 25 Apr 2025.

## Builds on (most cited first, 21 of 21)

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- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination](https://mlsanity.com/paper/1809.01852/) (2018)
- [Change Matters: Medication Change Prediction with Recurrent Residual Networks](https://mlsanity.com/paper/2105.01876/) (2021)
- [Conditional Generation Net for Medication Recommendation](https://mlsanity.com/paper/2202.06588/) (2022)
- [Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning](https://mlsanity.com/paper/1802.00662/) (2018)
- [SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation](https://mlsanity.com/paper/1710.05980/) (2017)
- [SelfCF: A Simple Framework for Self-supervised Collaborative Filtering](https://mlsanity.com/paper/2107.03019/) (2021)
- [Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation](https://mlsanity.com/paper/2301.12097/) (2023)
- [Drug Package Recommendation via Interaction-aware Graph Induction](https://mlsanity.com/paper/2102.03577/) (2021)
- [Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients](https://mlsanity.com/paper/2403.17745/) (2024)
- [Layer-refined Graph Convolutional Networks for Recommendation](https://mlsanity.com/paper/2207.11088/) (2022)
- [CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state](https://mlsanity.com/paper/2404.12228/) (2024)
- [MeSIN: Multilevel Selective and Interactive Network for Medication Recommendation](https://mlsanity.com/paper/2104.11026/) (2021)
- [StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation](https://mlsanity.com/paper/2308.16781/) (2023)
- [Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation](https://mlsanity.com/paper/2403.04503/) (2024)
- [Natural Language-Assisted Multi-modal Medication Recommendation](https://mlsanity.com/paper/2501.07166/) (2025)

## Abstract (arXiv)

Medication recommendation is crucial in healthcare, offering effective treatments based on patient's electronic health records (EHR). Previous studies show that integrating more medication-related knowledge improves medication representation accuracy. However, not all medications encompass multiple types of knowledge data simultaneously. For instance, some medications provide only textual descriptions without structured data. This imbalance in data availability limits the performance of existing models, a challenge we term the "bucket effect" in medication recommendation. Our data analysis uncovers the severity of the "bucket effect" in medication recommendation. To fill this gap, we introduce a cross-modal medication encoder capable of seamlessly aligning data from different modalities and propose a medication recommendation framework to integrate Multiple types of Knowledge, named MKMed. Specifically, we first pre-train a cross-modal encoder with contrastive learning on five knowledge modalities, aligning them into a unified space. Then, we combine the multi-knowledge medication representations with patient records for recommendations. Extensive experiments on the MIMIC-III and MIMIC-IV datasets demonstrate that MKMed mitigates the "bucket effect" in data, and significantly outperforms state-of-the-art baselines in recommendation accuracy and safety.