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

• URL: http://arxiv.org/abs/2504.18096v1
• Date: Fri, 25 Apr 2025 05:47:15 GMT
• Title: Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation
• Authors: Xiang Li, Haixu Ma, Guanyong Wu, Shi Mu, Chen Li, Shunpan Liang,
• Abstract summary: We introduce a cross-modal medication encoder capable of seamlessly aligning data from different modalities.<n>We then combine the multi-knowledge medication representations with patient records for recommendations.<n>Experiments show that MKMed mitigates the "bucket effect" in data, and significantly outperforms state-of-the-art baselines in recommendation accuracy and safety.
• Score: 7.962835256943926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Natural Language-Assisted Multi-modal Medication Recommendation [97.07805345563348]
  We introduce the Natural Language-Assisted Multi-modal Medication Recommendation(NLA-MMR)<n>The NLA-MMR is a multi-modal alignment framework designed to learn knowledge from the patient view and medication view jointly.<n>In this vein, we employ pretrained language models(PLMs) to extract in-domain knowledge regarding patients and medications.
  arXiv  Detail & Related papers  (2025-01-13T09:51:50Z)
• A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication Recommendation [50.43785306804359]
  We introduce a novel conTrastive prEtrain Model with Prompt Tuning (TEMPT) for multi-center medication recommendation.<n>We devise a novel prompt tuning method to capture the specific information of each hospital rather than adopting the common finetuning.<n>To validate the proposed model, we conduct extensive experiments on the public eICU multi-center medical dataset.
  arXiv  Detail & Related papers  (2024-12-28T06:12:02Z)
• Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients [47.68396964741116]
  We propose a novel model called Robust and Accurate REcommendations for Medication (RAREMed) to enhance accuracy for rare diseases. It employs a transformer encoder with a unified input sequence approach to capture complex relationships among disease and procedure codes. It provides accurate drug sets for both rare and common disease patients, thereby mitigating unfairness in medication recommendation systems.
  arXiv  Detail & Related papers  (2024-03-26T14:36:22Z)
• CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning [10.60553153370577]
  Medication recommendation aims to integrate patients' long-term health records to provide accurate and safe medication combinations. Existing methods often fail to deeply explore the true causal relationships between diseases/procedures and medications. We propose the Causal Inference-driven Dual-Granularity Medication Recommendation method (CIDGMed)
  arXiv  Detail & Related papers  (2024-03-01T08:50:27Z)
• StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation [9.296433860766165]
  StratMed is a stratification strategy that overcomes the long-tailed problem and achieves fuller learning of sparse data. It also utilizes a dual-property network to address the issue of mutual constraints on the safety and accuracy of medication combinations. Our model reduces safety risk by 15.08%, improves accuracy by 0.36%, and reduces training time consumption by 81.66%.
  arXiv  Detail & Related papers  (2023-08-31T14:59:32Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)
• Conditional Generation Net for Medication Recommendation [73.09366442098339]
  Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics. We propose Conditional Generation Net (COGNet) which introduces a novel copy-or-predict mechanism to generate the set of medicines. We validate the proposed model on the public MIMIC data set, and the experimental results show that the proposed model can outperform state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-02-14T10:16:41Z)
• MeSIN: Multilevel Selective and Interactive Network for Medication Recommendation [9.173903754083927]
  We propose a multilevel selective and interactive network (MeSIN) for medication recommendation. First, an attentional selective module (ASM) is applied to assign flexible attention scores to different medical codes embeddings. Second, we incorporate a novel interactive long-short term memory network (InLSTM) to reinforce the interactions of multilevel medical sequences in EHR data.
  arXiv  Detail & Related papers  (2021-04-22T12:59:50Z)
• Soft-Label Anonymous Gastric X-ray Image Distillation [49.24576562557866]
  This paper presents a soft-label anonymous gastric X-ray image distillation method based on a gradient descent approach. Experimental results show that the proposed method can not only effectively compress the medical dataset but also anonymize medical images to protect the patient's private information.
  arXiv  Detail & Related papers  (2021-04-07T02:04:12Z)
• PREMIER: Personalized REcommendation for Medical prescrIptions from Electronic Records [8.365167718547296]
  We design a two-stage attention-based personalized medication recommender system called PREMIER. Our system takes into account the interactions among drugs in order to minimize the adverse effects for the patient. Experiment results on MIMIC-III and a proprietary outpatient dataset show that PREMIER outperforms state-of-the-art medication recommendation systems.
  arXiv  Detail & Related papers  (2020-08-28T04:48:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.