Contrastive Learning on Medical Intents for Sequential Prescription Recommendation

TL;DR

This paper introduces ARCI, a transformer-based contrastive learning method that captures diverse medical intents and temporal relationships in sequential prescription data, improving recommendation accuracy and interpretability.

Contribution

It proposes a novel intent-aware contrastive learning approach with multi-head transformers to model coexisting health profiles in sequential prescription recommendation.

Findings

ARCI outperforms state-of-the-art methods on real-world datasets.

The model provides interpretable insights for healthcare practitioners.

It effectively captures diverse temporal health profiles.

Abstract

Recent advancements in sequential modeling applied to Electronic Health Records (EHR) have greatly influenced prescription recommender systems. While the recent literature on drug recommendation has shown promising performance, the study of discovering a diversity of coexisting temporal relationships at the level of medical codes over consecutive visits remains less explored. The goal of this study can be motivated from two perspectives. First, there is a need to develop a sophisticated sequential model capable of disentangling the complex relationships across sequential visits. Second, it is crucial to establish multiple and diverse health profiles for the same patient to ensure a comprehensive consideration of different medical intents in drug recommendation. To achieve this goal, we introduce Attentive Recommendation with Contrasted Intents (ARCI), a multi-level transformer-based method designed to capture the different but coexisting temporal paths across a shared sequence of visits. Specifically, we propose a novel intent-aware method with contrastive learning, that links specialized medical intents of the patients to the transformer heads for extracting distinct temporal paths associated with different health profiles. We conducted experiments on two real-world datasets for the prescription recommendation task using both ranking and classification metrics. Our results demonstrate that ARCI has outperformed the state-of-the-art prescription recommendation methods and is capable of providing interpretable insights for healthcare practitioners.