The role of taxonomies in social media and the semantic web for health education: A study of SNOMED CT terms in youtube health video tags

S. Konstantinidis, Luis Fernandez, Panagiotis D. Bamidis, R. Karlsen

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Background: An increasing amount of health education resources for patients and professionals are distributed via social media channels. For example, thousands of health education videos are disseminated via You- Tube. Often, tags are assigned by the disseminator. However, the lack of use of standardized terminologies in those tags and the presence of misleading videos make it particularly hard to retrieve relevant videos. Objectives: i) Identify the use of standardized medical thesauri (SNOMED CT) in You- Tube Health videos tags from preselected YouTube Channels and demonstrate an information technology (IT) architecture for treating the tags of these health (video) resources. ii) Investigate the relative percentage of the tags used that relate to SNOMED CT terms. As such resources may play a key role in educating professionals and patients, the use of standardized vocabularies may facilitate the sharing of such resources. iii) Demonstrate how such resources may be properly exploited within the new generation of semantically enriched content or learning management systems that allow for knowledge expansion through the use of linked medical data and numerous literature resources also described through the same vocabularies. Methods: We implemented a video portal integrating videos from 500 US Hospital channels. The portal integrated 4,307 YouTube videos regarding surgery as described by 64,367 tags. BioPortal REST services were used within our portal to match SNOMED CT terms with YouTube tags by both exact match and non-exact match. The whole architecture was complemented with a mechanism to enrich the retrieved video resources with other educational material residing in other repositories by following contemporary semantic web advances, in the form of Linked Open Data (LOD) principles. Results: The average percentage of YouTube tags that were expressed using SNOMED CT terms was about 22.5%, while one third of YouTube tags per video contained a SNOMED CT term in a loose search; this analogy became one tenth in the case of exact match. Retrieved videos were then linked further to other resources by using LOD compliant systems. Such results were exemplified in the case of systems and technologies used in the mEducator EC funded project. Conclusion: YouTube Health videos can be searched for and retrieved using SNOMED CT terms with a high possibility of identifying health videos that users want based on their search criteria. Despite the fact that tagging of this information with SNOMED CT terms may vary, its availability and linked data capacity opens the door to new studies for personalized retrieval of content and linking with other knowledge through linked medical data and semantic advances in (learning) content management systems.

Original languageEnglish
Pages (from-to)168-179
Number of pages12
JournalMethods of Information in Medicine
Volume52
Issue number2
DOIs
Publication statusPublished - 2013
Externally publishedYes

Fingerprint

Systematized Nomenclature of Medicine
Social Media
Health Education
Semantics
Health
Vocabulary
Health Resources
Learning
Controlled Vocabulary
Technology
Patient Education
Terminology
Information Systems

Keywords

  • Educational metadata
  • Heath video
  • Learning/ content management system
  • Linked data
  • Linked medical data
  • Medical thesauri
  • SNOMED CT
  • Tag cloud
  • Tags
  • Terms

ASJC Scopus subject areas

  • Health Informatics
  • Advanced and Specialised Nursing
  • Health Information Management

Cite this

The role of taxonomies in social media and the semantic web for health education : A study of SNOMED CT terms in youtube health video tags. / Konstantinidis, S.; Fernandez, Luis; Bamidis, Panagiotis D.; Karlsen, R.

In: Methods of Information in Medicine, Vol. 52, No. 2, 2013, p. 168-179.

Research output: Contribution to journalArticle

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