Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings

TL;DR

This paper introduces a health recommender system that leverages hyperbolic embeddings of medical codes to improve patient-doctor matching, demonstrating enhanced accuracy and practical benefits in healthcare personalization.

Contribution

It presents a novel content-based recommender system utilizing hyperbolic Poincare embeddings of ICD-9 codes for improved healthcare recommendations.

Findings

Outperforms traditional models in recommendation accuracy

Enables personalized patient-doctor matchmaking

Utilizes transfer learning with pre-trained embeddings

Abstract

In contrast to many other domains, recommender systems in health services may benefit particularly from the incorporation of health domain knowledge, as it helps to provide meaningful and personalised recommendations catering to the individual's health needs. With recent advances in representation learning enabling the hierarchical embedding of health knowledge into the hyperbolic Poincare space, this work proposes a content-based recommender system for patient-doctor matchmaking in primary care based on patients' health profiles, enriched by pre-trained Poincare embeddings of the ICD-9 codes through transfer learning. The proposed model outperforms its conventional counterpart in terms of recommendation accuracy and has several important business implications for improving the patient-doctor relationship.