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Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

Author

Listed:
  • Yao Cai

    (School of Information and Library Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Fei Yu

    (School of Information and Library Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Carolina Health Informatics Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

  • Manish Kumar

    (Public Health Leadership Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

  • Roderick Gladney

    (Carolina Health Informatics Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

  • Javed Mostafa

    (School of Information and Library Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Carolina Health Informatics Program, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

Abstract

A health recommender system (HRS) provides a user with personalized medical information based on the user’s health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management.

Suggested Citation

  • Yao Cai & Fei Yu & Manish Kumar & Roderick Gladney & Javed Mostafa, 2022. "Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review," IJERPH, MDPI, vol. 19(22), pages 1-15, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:22:p:15115-:d:974595
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    References listed on IDEAS

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    1. Martin Michalowski & Szymon Wilk & Wojtek Michalowski & Dympna O’Sullivan & Silvia Bonaccio & Enea Parimbelli & Marc Carrier & Grégoire Le Gal & Stephen Kingwell & Mor Peleg, 2021. "A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment," IJERPH, MDPI, vol. 18(14), pages 1-28, July.
    2. Andreas Meier & Luis Terán, 2019. "eDemocracy & eGovernment," Progress in IS, Springer, edition 2, number 978-3-030-17585-6, March.
    3. Yaroslava Robles-Bykbaev & Christian Oyola-Flores & Vladimir Espartaco Robles-Bykbaev & Martín López-Nores & Paola Ingavélez-Guerra & José Juan Pazos-Arias & Fernando Pesántez-Avilés & Manuel Ramos-Ca, 2019. "A Bespoke Social Network for Deaf Women in Ecuador to Access Information on Sexual and Reproductive Health," IJERPH, MDPI, vol. 16(20), pages 1-17, October.
    4. Martin Wiesner & Daniel Pfeifer, 2014. "Health Recommender Systems: Concepts, Requirements, Technical Basics and Challenges," IJERPH, MDPI, vol. 11(3), pages 1-28, March.
    5. Hoill Jung & Kyungyong Chung, 2016. "Knowledge-based dietary nutrition recommendation for obese management," Information Technology and Management, Springer, vol. 17(1), pages 29-42, March.
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    Cited by:

    1. Tatiana V. Afanasieva & Pavel V. Platov & Andrey V. Komolov & Andrey V. Kuzlyakin, 2024. "Leveraging ChatGPT and Long Short-Term Memory in Recommender Algorithm for Self-Management of Cardiovascular Risk Factors," Mathematics, MDPI, vol. 12(16), pages 1-28, August.

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