Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings
This work addresses personalized healthcare recommendations for patients and doctors, but it is incremental as it applies existing hyperbolic embedding techniques to a specific domain.
The paper tackled the problem of patient-doctor matchmaking in primary care by incorporating health domain knowledge using hyperbolic embeddings, resulting in improved recommendation accuracy over conventional methods.
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.