Incorporating domain knowledge into health recommender systems using hyperbolic embeddings

Joel Peito, Qiwei Han

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 Poincaré 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 Poincaré 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.

Original languageEnglish
Title of host publicationComplex Networks and Their Applications IX
Subtitle of host publicationProceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020
EditorsRosa M. Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis Mateus Rocha, Marta Sales-Pardo
Place of PublicationChan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages130-141
Number of pages12
Volume2
ISBN (Print)9783030653507
DOIs
Publication statusPublished - 2021
Event9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020 - Madrid, Spain
Duration: 1 Dec 20203 Dec 2020

Publication series

NameStudies in Computational Intelligence
Volume944
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020
CountrySpain
CityMadrid
Period1/12/203/12/20

Keywords

  • Health recommender systems
  • International classification of diseases
  • Patient-doctor relationship
  • Poincaré embeddings
  • Primary care

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