HealthRecSys: A semantic content-based recommender system to complement health videos
<h3>Background</h3><p dir="ltr">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, vi...
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2017
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| _version_ | 1864513557123563520 |
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| author | Carlos Luis Sanchez Bocanegra (19691665) |
| author2 | Jose Luis Sevillano Ramos (19691668) Carlos Rizo (4797600) Anton Civit (18629749) Luis Fernandez-Luque (3572423) |
| author2_role | author author author author |
| author_facet | Carlos Luis Sanchez Bocanegra (19691665) Jose Luis Sevillano Ramos (19691668) Carlos Rizo (4797600) Anton Civit (18629749) Luis Fernandez-Luque (3572423) |
| author_role | author |
| dc.creator.none.fl_str_mv | Carlos Luis Sanchez Bocanegra (19691665) Jose Luis Sevillano Ramos (19691668) Carlos Rizo (4797600) Anton Civit (18629749) Luis Fernandez-Luque (3572423) |
| dc.date.none.fl_str_mv | 2017-05-15T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/s12911-017-0431-7 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/HealthRecSys_A_semantic_content-based_recommender_system_to_complement_health_videos/27050707 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Patient Education Health Recommender System Natural Language Processing Information Retrieval |
| dc.title.none.fl_str_mv | HealthRecSys: A semantic content-based recommender system to complement health videos |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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 information retrieval metrics such as the normalized discounted cumulative gain and precision at K.</p><h3>Results</h3><p dir="ltr">The 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.</p><h3>Conclusions</h3><p dir="ltr">Our 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.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Medical Informatics and Decision Making<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12911-017-0431-7" target="_blank">https://dx.doi.org/10.1186/s12911-017-0431-7</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_640a7163ab29cc9296ecc1f29a776f1c |
| identifier_str_mv | 10.1186/s12911-017-0431-7 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27050707 |
| publishDate | 2017 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | HealthRecSys: A semantic content-based recommender system to complement health videosCarlos Luis Sanchez Bocanegra (19691665)Jose Luis Sevillano Ramos (19691668)Carlos Rizo (4797600)Anton Civit (18629749)Luis Fernandez-Luque (3572423)Health sciencesHealth services and systemsPatient EducationHealth Recommender SystemNatural Language ProcessingInformation Retrieval<h3>Background</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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 information retrieval metrics such as the normalized discounted cumulative gain and precision at K.</p><h3>Results</h3><p dir="ltr">The 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.</p><h3>Conclusions</h3><p dir="ltr">Our 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.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Medical Informatics and Decision Making<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12911-017-0431-7" target="_blank">https://dx.doi.org/10.1186/s12911-017-0431-7</a></p>2017-05-15T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s12911-017-0431-7https://figshare.com/articles/journal_contribution/HealthRecSys_A_semantic_content-based_recommender_system_to_complement_health_videos/27050707CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270507072017-05-15T03:00:00Z |
| spellingShingle | HealthRecSys: A semantic content-based recommender system to complement health videos Carlos Luis Sanchez Bocanegra (19691665) Health sciences Health services and systems Patient Education Health Recommender System Natural Language Processing Information Retrieval |
| status_str | publishedVersion |
| title | HealthRecSys: A semantic content-based recommender system to complement health videos |
| title_full | HealthRecSys: A semantic content-based recommender system to complement health videos |
| title_fullStr | HealthRecSys: A semantic content-based recommender system to complement health videos |
| title_full_unstemmed | HealthRecSys: A semantic content-based recommender system to complement health videos |
| title_short | HealthRecSys: A semantic content-based recommender system to complement health videos |
| title_sort | HealthRecSys: A semantic content-based recommender system to complement health videos |
| topic | Health sciences Health services and systems Patient Education Health Recommender System Natural Language Processing Information Retrieval |