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

BMC Medical Informatics and Decision Making

Research Article

Carlos Luis Sanchez Bocanegra¹  Jose Luis Sevillano Ramos¹  Anton Civit¹  Luis Fernandez-Luque²  Carlos Rizo³ 

[1] Department of Architecture and Computer Technology Universidad de Sevilla, Seville, Spain;Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar Foundation, PO Box 5825, Doha, Qatar;eHealth Researcher, Toronto, Canada;

关键词: Patient Education;    Health Recommender System;    Natural Language Processing;    Information Retrieval;   

DOI  :  10.1186/s12911-017-0431-7

 received in 2016-11-11, accepted in 2017-03-24,  发布年份 2017

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s). 2017