ArticleBMC medical informatics and decision making2017
HealthRecSys: A semantic content-based recommender system to complement health videos.
Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 88 citations in OpenAlex.
- Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review.Journal of medical Internet research · 2024Pooled it
- Health Recommender Systems: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Diet-Related Health Recommender Systems for Patients With Chronic Health Conditions: Scoping Review.Journal of medical Internet research · 2026Article
- How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- The Implementation of Recommender Systems for Mental Health Recovery Narratives: Evaluation of Use and Performance.JMIR mental health · 2024Article
- Using AI-ML to Augment the Capabilities of Social Media for Telehealth and Remote Patient Monitoring.Healthcare (Basel, Switzerland) · 2023Article
- Web-Based Patient Recommender Systems for Preventive Care: Protocol for Empirical Research Propositions.JMIR research protocols · 2023Article
- Drug Recommendation System for Diabetes Using a Collaborative Filtering and Clustering Approach: Development and Performance Evaluation.Journal of medical Internet research · 2022Article
- Diabetes and conversational agents: the AIDA project case study.Discover artificial intelligence · 2021Article
- Digital Health for Supporting Precision Medicine in Pediatric Endocrine Disorders: Opportunities for Improved Patient Care.Frontiers in pediatrics · 2021Review
- Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.PloS one · 2021Article
- A minimum data set of user profile or electronic health record for chemical warfare victims' recommender system.Journal of family medicine and primary care · 2020Article
- Using Natural Language Processing Techniques to Provide Personalized Educational Materials for Chronic Disease Patients in China: Development and Assessment of a Knowledge-Based Health Recommender System.JMIR medical informatics · 2020Article
- Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol.BMC public health · 2018Article
- How recommender systems could support and enhance computer-tailored digital health programs: A scoping review.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsThe 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 information retrieval metrics such as the normalized discounted cumulative gain and precision at K.
resultsThe majority of websites recommended by our system for health videos were relevant, based on ratings by health professionals. The normalized discounted cumulative gain was between 46% and 90% for the different topics.
conclusionsOur study demonstrates the feasibility of using a semantic content-based recommender system to enrich YouTube health videos. Evaluation with end-users, in addition to healthcare professionals, will be required to identify the acceptance of these recommendations in a nonsimulated information-seeking context.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.