@inproceedings{a911805125644fa99b45a44c8e960b6c,
title = "Incorporating domain knowledge into health recommender systems using hyperbolic 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{\textquoteright}s health needs. With recent advances in representation learning enabling the hierarchical embedding of health knowledge into the hyperbolic Poincar{\'e} space, this work proposes a content-based recommender system for patient-doctor matchmaking in primary care based on patients{\textquoteright} health profiles, enriched by pre-trained Poincar{\'e} 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.",
keywords = "Health recommender systems, International classification of diseases, Patient-doctor relationship, Poincar{\'e} embeddings, Primary care",
author = "Joel Peito and Qiwei Han",
year = "2021",
doi = "10.1007/978-3-030-65351-4_11",
language = "English",
isbn = "9783030653507",
volume = "2",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "130--141",
editor = "Benito, {Rosa M.} and Chantal Cherifi and Hocine Cherifi and Esteban Moro and Rocha, {Luis Mateus} and Marta Sales-Pardo",
booktitle = "Complex Networks and Their Applications IX",
address = "Germany",
note = "9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020 ; Conference date: 01-12-2020 Through 03-12-2020",
}