User-Adaptive Meta-Learning for Cold-Start Medication Recommendation with Uncertainty Filtering

2026-01-30

AI Summary

This work addresses the cold-start problem in medication recommendation for patients lacking prior prescription history in electronic health records. To tackle this challenge, the authors propose MetaDrug, a framework that leverages a dual-level meta-learning mechanism—combining self-adaptation and peer-adaptation—to personalize treatment predictions using both a patient’s own longitudinal visit records and those of clinically similar patients. An uncertainty quantification module is integrated to select high-quality support samples, while temporal dynamics are captured through sequential modeling. Furthermore, medical knowledge graph embeddings are incorporated to enrich clinical context. Evaluated on the MIMIC-III and AKI datasets, MetaDrug significantly outperforms existing approaches, demonstrating enhanced accuracy and robustness in cold-start medication recommendation scenarios.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Graph-based Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Large-scale Electronic Health Record (EHR) databases have become indispensable in supporting clinical decision-making through data-driven treatment recommendations. However, existing medication recommender methods often struggle with a user (i.e., patient) cold-start problem, where recommendations for new patients are usually unreliable due to the lack of sufficient prescription history for patient profiling. While prior studies have utilized medical knowledge graphs to connect medication concepts through pharmacological or chemical relationships, these methods primarily focus on mitigating the item cold-start issue and fall short in providing personalized recommendations that adapt to individual patient characteristics. Meta-learning has shown promise in handling new users with sparse interactions in recommender systems. However, its application to EHRs remains underexplored due to the unique sequential structure of EHR data. To tackle these challenges, we propose MetaDrug, a multi-level, uncertainty-aware meta-learning framework designed to address the patient cold-start problem in medication recommendation. MetaDrug proposes a novel two-level meta-adaptation mechanism, including self-adaptation, which adapts the model to new patients using their own medical events as support sets to capture temporal dependencies; and peer-adaptation, which adapts the model using similar visits from peer patients to enrich new patient representations. Meanwhile, to further improve meta-adaptation outcomes, we introduce an uncertainty quantification module that ranks the support visits and filters out the unrelated information for adaptation consistency. We evaluate our approach on the MIMIC-III and Acute Kidney Injury (AKI) datasets. Experimental results on both datasets demonstrate that MetaDrug consistently outperforms state-of-the-art medication recommendation methods on cold-start patients.

Problem

• cold-start
• medication recommendation
• personalized recommendation
• Electronic Health Records
• new patient

Innovation

• meta-learning
• cold-start
• medication recommendation
• uncertainty quantification
• electronic health records