Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation
Journal:
arXiv
Published Date:
Apr 25, 2025
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.
