Self-supervised Hierarchical Representation for Medication Recommendation

TL;DR

This paper introduces HIER, a hierarchical encoder that captures latent medical term structures using self-supervised learning and position encoding, significantly improving medication recommendation accuracy.

Contribution

It presents a novel hierarchical encoding method for medical codes that leverages self-supervised learning and position encoding to enhance recommendation performance.

Findings

Significant improvement in recommendation accuracy

Effective modeling of hierarchical medical code structures

Compatibility with existing recommendation methods

Abstract

Medication recommender is to suggest appropriate medication combinations based on a patient's health history, e.g., diagnoses and procedures. Existing works represent different diagnoses/procedures well separated by one-hot encodings. However, they ignore the latent hierarchical structures of these medical terms, undermining the generalization performance of the model. For example, "Respiratory Diseases", "Chronic Respiratory Diseases" and "Chronic Bronchiti" have a hierarchical relationship, progressing from general to specific. To address this issue, we propose a novel hierarchical encoder named HIER to hierarchically represent diagnoses and procedures, which is based on standard medical codes and compatible with any existing methods. Specifically, the proposed method learns relation embedding with a self-supervised objective for incorporating the neighbor hierarchical structure. Additionally, we develop the position encoding to explicitly introduce global hierarchical position. Extensive experiments demonstrate significant and consistent improvements in recommendation accuracy across four baselines and two real-world clinical datasets.