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.
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Notes
- 1.
International Classification of Diseases (ICD) is a comprehensive standard of diseases or medical conditions maintained by the WHO and widely used among healthcare organizations worldwide. It is revised periodically and now in its 10th version (known as ICD-10). However, the ICD-code used this study is still in the 9th version (ICD-9).
- 2.
SNOMED CT refers to Systematized NOmenclature of MEDicine Clinical Terms that is used to encode healthcare terminology for electronic health records. All UMLS data including SNOMED CT and CUIs have been retrieved from the US National Library of Medicine (NLM).
- 3.
We emphasise that all proposed models are entirely CB, hence neglecting the similarity of interactions between patients or doctors. As this research is preliminary, we acknowledge that adding interaction data in a hybrid approach may boost performance substantially.
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Acknowledgements
This work was funded by Fundação para a Ciência e a Tecnologia (UID/ECO/00124/2019, UIDB/00124/2020 and Social Sciences Data Lab, PINFRA/22209/2016), POR Lisboa and POR Norte (Social Sciences Data Lab, PINFRA/22209/2016).
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Peito, J., Han, Q. (2021). Incorporating Domain Knowledge into Health Recommender Systems Using Hyperbolic Embeddings. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-65351-4_11
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