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

Yao Cai1,2, Fei Yu1,3, Manish Kumar4

  • 1School of Information and Library Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

International Journal of Environmental Research and Public Health
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

This review summarizes health recommender system (HRS) development over a decade. Knowledge-based approaches are common, but research needs to expand health domains and evaluation sample sizes for better healthcare decision-making support.

Keywords:
health recommender systemrecommendation technologyresearch areasystem evaluationuser model

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Area of Science:

  • Health Informatics
  • Information Systems
  • Computer Science

Background:

  • Health recommender systems (HRS) offer personalized medical information using user health profiles.
  • Recent advancements aim to enhance user engagement and health management through tailored recommendations.

Purpose of the Study:

  • To conduct a scoping review of health recommender system (HRS) development from 2010-2022.
  • To identify trends and gaps in HRS research across five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation.

Main Methods:

  • Systematic search of major scientific databases (PubMed, ACM, IEEE, Web of Science, Scopus).
  • Inclusion of English literature published between 2010 and 2022.
  • Data extraction and analysis guided by the PRISMA extension for Scoping Reviews.

Main Results:

  • Sixty-three studies were included, covering 24 health domains and 10 types of recommended items.
  • Knowledge-based approaches were the most prevalent recommendation technology.
  • Fifty-nine studies reported system evaluations using various methods and metrics, though sample sizes were often limited.

Conclusions:

  • Despite progress, HRS research shows limitations in health domain diversity, recommended item scope, and evaluation sample sizes.
  • Future research should prioritize dynamic user modeling, open-source knowledge bases, and large-scale efficacy evaluations.
  • Improving HRS performance is crucial for empowering users in healthcare decision-making and self-management.