HealthRecSys: A semantic content-based recommender system to complement health videos

BMC Medical Informatics and Decision Making
Carlos Luis Sanchez BocanegraLuis Fernandez-Luque

Abstract

The Internet, and its popularity, continues to grow at an unprecedented pace. Watching videos online is very popular; it is estimated that 500 h of video are uploaded onto YouTube, a video-sharing service, every minute and that, by 2019, video formats will comprise more than 80% of Internet traffic. Health-related videos are very popular on YouTube, but their quality is always a matter of concern. One approach to enhancing the quality of online videos is to provide additional educational health content, such as websites, to support health consumers. This study investigates the feasibility of building a content-based recommender system that links health consumers to reputable health educational websites from MedlinePlus for a given health video from YouTube. The dataset for this study includes a collection of health-related videos and their available metadata. Semantic technologies (such as SNOMED-CT and Bio-ontology) were used to recommend health websites from MedlinePlus. A total of 26 healths professionals participated in evaluating 253 recommended links for a total of 53 videos about general health, hypertension, or diabetes. The relevance of the recommended health websites from MedlinePlus to the videos was measured using i...Continue Reading

References

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Citations

Sep 29, 2020·Journal of Family Medicine and Primary Care·Elham AalipourHamid Beigy
Jun 30, 2021·Journal of Medical Internet Research·Robin De CroonKatrien Verbert

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Software Mentioned

R application
Annotator
SNOMED
irr
Medline Plus
MedlinePlus
HealthRecSys
ontology
Bio
UmlConcept

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