  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.
