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Methods Inf Med 2013; 52(02): 168-179
DOI: 10.3414/ME12-02-0005
Focus Theme – Original Articles
Schattauer GmbH

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
1   Aristotle University of Thessaloniki, Lab of Medical Informatics, Medical School, Thessaloniki, Greece
,
L. Fernandez-Luque
2   Norut, Tromsø, Norway
,
P. Bamidis
3   Aristotle University of Thessaloniki, Lab of Medical Informatics, Medical School, Thessaloniki, Greece
,
R. Karlsen
2   Norut, Tromsø, Norway
4   University of Tromsø, Computer Science Department, Tromsø, Norway
› Author Affiliations
Further Information

Publication History

received: 04 March 2012

accepted: 03 February 2013

Publication Date:
20 January 2018 (online)

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Summary

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.

Keywords

Heath video - tags - tag cloud - SNOMED CT - terms - Medical thesauri - linked data - linked medical data - educational metadata - learn-ing/content management system
 

  • References

  • 1 Paton C, Bamidis PD, Eysenbach G, Hansen M, Cabrer M. Experience in the Use of Social Media in Medical and Health Education Contribution of the IMIA Social Media Working Group. Yearb Med Inform 2011; 6 (01) 21-29.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 2 Lau AY, Siek KA, Fernandez-Luque L, Tange H, Chhanabhai P, Li SY. et al The Role of Social Media for Patients and Consumer Health. Yearb Med Inform 2011; 6 (01) 131-138.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 3 Eysenbach G, Powell J, Kuss O, Sa ER. Empirical studies assessing the quality of health information for consumers on the world wide web: a systematic review. JAMA 2002; 287 (20) 2691-2700.
    CrossrefPubMedGoogle Scholar
  • 4 Agichtein E, Castillo C, Donato D, Gionis A, Mishne G. Finding high-quality content in social media. Proceedings of the international conference on Web search and web data mining (WSDM ’08), 2008 Feb 11–12; Palo Alto, California, USA. 2008: 183-194.
    Google Scholar
  • 5 Mayer MA, Karkaletsis V, Stamatakis K, Leis A, Villarroel D, Thomeczek C. et al MedIEQ-Quality labelling of medical web content using multilingual information extraction. Stud Health Technol Inform 2006; 121: 183-190.
    PubMedGoogle Scholar
  • 6 Bamidis E, Kaldoudi C. Pattichis. From taxonomies to folksonomies: a roadmap from formal to informal modeling of medical concepts and objects, Proceedings of the 9th International Conference on Information Technology and Applications in Biomedicine (ITAB2009), 2009 Nov 5–7; Larnaca, Cyprus. 2009.
    Google Scholar
  • 7 Smith CA, Wicks PJ. PatientsLikeMe: Consumer Health Vocabulary as a Folksonomy. AMIA Annu Symp Proc. 2008: 682-686.
    Google Scholar
  • 8 US Department of Health and Human Services. Health Information Technology: Initial Set of Standards, Implementation Specifications, and Certification Criteria for Electronic Health Record Technology: Final Rule. 45 CFR Part 170 Federal Register, July 28, 2010.
    Google Scholar
  • 9 Jones P. The UK Edition of SNOMED CT as the Fundamental Standard for Clinical Terminology within the NHS in England. Department of Health Informatics Directorate. 2011.
    Google Scholar
  • 10 Conley E, Benson T. SNOMED CT: Who Needs to Know What?. EJBI 2011; 7 (02) 40-47.
    PubMedGoogle Scholar
  • 11 International Health Terminology Standards Development Organisation. SNOMED Clinical Terms User Guide - July 2008. International Release. International Health Terminology Standards Development Organisation. 2008.
    PubMedGoogle Scholar
  • 12 Bakhshi-Raiez F, Ahmadian L, Cornet R, de Jonge E, de Keizer NF. Construction of an interface terminology on SNOMED CT. Generic approach and its application in intensive care. Methods Inf Med 2010; 49 (04) 349-359.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 13 Rosenbloom ST, Miller RA, Johnson KB, Elkin PL, Brown SH. Interface terminologies: facilitating direct entry of clinical data into electronic health record systems. J Am Med Inform Assoc 2006; 13 (03) 277-288.
    CrossrefPubMedGoogle Scholar
  • 14 Spackman KA, Campbell KE, Côté RA. SNOMED RT: a reference terminology for health care. Proc AMIA Annu Fall Symp. 1997: 640-644.
    Google Scholar
  • 15 Oemig F, Blobel B. Semantic interoperability adheres to proper models and code systems. A detailed examination of different approaches for score systems. Methods Inf Med 2010; 49 (02) 148-155.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 16 Elkin PL, Brown SH, Bauer BA, Husser CS, Carruth W, Bergstrom LR. et al A controlled trial of automated classification of negation from clinical notes. BMC Med Inform Decis Mak 2005; 5 (01) 13
    CrossrefPubMedGoogle Scholar
  • 17 Lee DH, Lau FY, Quan H. A method for encoding clinical datasets with SNOMED CT. BMC Med Inform Decis Ma 2010; 10: 53
    PubMedGoogle Scholar
  • 18 Rector AL. Thesauri and formal classifications: terminologies for people and machines. Methods Inf Med 1998; 37: 501-509.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 19 Wang Y, Patrick J, Miller G, O’Hallaran J. A computational linguistics motivated mapping of ICPC-2 PLUS to SNOMED CT. BMC Med Inform Decis Mak 2008; 8 Suppl (Suppl. 01) S5
    CrossrefPubMedGoogle Scholar
  • 20 Wade G, Rosenbloom ST. Experiences mapping a legacy interface terminology to SNOMED CT. BMC Med Inform Decis Mak 2008; 8 Suppl (Suppl. 01) S3
    CrossrefPubMedGoogle Scholar
  • 21 Aronson AR. Effective mapping of biomedical text to the UMLS metathesaurus: the MetaMap program. Proc AMIA Sym. 2001: 17-21.
    Google Scholar
  • 22 Park HA, Lundberg C, Coenen A, Konicek D. Evaluation of the content coverage of SNOMED CT representing ICNP seven-axis version 1 concepts. Methods Inf Med 2011; 50 (05) 472-478.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 23 Heymans S, McKennirey M, Phillips J. Semantic validation of the use of SNOMED CT in HL7 clinical documents. J Biomed Semantics. 2011; 2 (01) 2
    PubMedGoogle Scholar
  • 24 Bamidis PD, Kaldoudi E, Pattichis C. mEducator: A Best Practice Network for Re-purposing and Sharing Medical Educational Multi-Type Content. Proceedings of the 10th IFIP Working Conference on Virtual Enterprises (PRO-VE’09); 2009 Oct 7–9; Thessaloniki, Greece.Springer. 2009; 769-776.
    PubMedGoogle Scholar
  • 25 Konstantinidis S, Kaldoudi E, Bamidis P. Enabling Content Sharing in Contemporary Medical Education: A Review of Technical Standards. The Journal on Information Technology in Healthcare 2009; 7 (06) 363-75.
    PubMedGoogle Scholar
  • 26 Kaldoudi E, Konstantinidis S, Bamidis P. Web 2.0 Approaches for Active, Collaborative Learning in Medicine and Health. In Mohammed S, Fiaidhi J. editors Ubiquitous Health and Medical Informatics: The Ubiquity 2.0 Trend and Beyond. Hershey, PA, USA: IGI Global; 2010. (chapter 7)
    Google Scholar
  • 27 Kaldoudi E, Konstantinidis S, Bamidis P. Web Advances in Education: Interactive, Collaborative Learning via Web 2.0”. In Tzanavari A, Tsapatsoulis N. editors Affective, Interactive and Cognitive Methods for E-Learning Design: Creating an Optimal Education Experience. Hershey, PA, USA: Information Science Reference, IGI Global; 2010: 32-50. (chapter 2)
    CrossrefGoogle Scholar
  • 28 Rainie L. Tagging. Washington, DC: Pew Internet American Life Project. 2007. Jan 31 (cited 2012 Feb 28). Available from http://www.pewinternet.org/~/media//Files/Reports/2007/PIP_Tagging.pdf.pdf.
    Google Scholar
  • 29 Fox S, Jones S. The social life of health information. Washington, DC: Pew Internet American Life Project. 2009. Jun 11 (cited 2012 Feb 28) Available from http://www.pewinternet.org/~/media//Files/Reports/2009/PIP_Health_2009.pdf.
    Google Scholar
  • 30 Berendt B, Hanser C. Tags are not Metadata, but “Just More Content” - to Some People. Proceedings of the Proceedings of the International Conference on Weblogs and Social Media (ICWSM’2007). 2007. Mar 26-28. Boulder, Colorado, USA: 2007.
    Google Scholar
  • 31 Doing-Harris KM, Zeng-Treitler Q. Computer-Assisted Update of a Consumer Health Vocabulary Through Mining of Social Network Data. J Med Internet Res 2011; 13 (02) e37
    CrossrefPubMedGoogle Scholar
  • 32 Khan SA, McFarlane DJ, Li J, Ancker JS, Hutchinson C, Cohall A, Kukafka R. Healthy Harlem: empowering health consumers through social networking, tailoring and web 2.0 technologies. AMIA Annu Symp Proc. 2007: 1007
    Google Scholar
  • 33 Ding Y, Jacob EK, Fried M, Toma I, Yan E, Foo S, Milojeviæ S. Upper Tag Ontology for Integrating Social Tagging Data. J. Am. Soc. Inf. Sci. Technol 2010; 61 (03) 505-521.
    PubMedGoogle Scholar
  • 34 Bizer C, Heath T, Idehen K, Berners-Lee T. Linked Data on the Web (LDOW2008). Proceeding of the 17th international conference on World Wide Web; 2008; Beijing, China. 2008: 1265-1266.
    Google Scholar
  • 35 Bizer C, Heath T, Berners-Lee T. Linked Data - The Story So Far. International Journal on Semantic Web and Information Systems (IJSWIS) 2009; 5 (03) 1-22.
    PubMedGoogle Scholar
  • 36 Berners-Lee T. 2006. Linked Data (cited 2012 Sep 20). Available from. http://www.w3.org/DesignIssues/LinkedData.html.
    Google Scholar
  • 37 Agazio J, Buckley KM. An untapped resource: using YouTube in nursing education. Nurse Educ 2009; 34 (01) 23-28.
    CrossrefPubMedGoogle Scholar
  • 38 Clifton A, Mann C. Can YouTube enhance student nurse learning?. Nurse Education Today 2011; 31 (04) 311-313.
    CrossrefPubMedGoogle Scholar
  • 39 Skiba DJ. Nursing education 2.0: YouTube. Nurs Educ Perspect 28 (02) 100-102.
    PubMedGoogle Scholar
  • 40 Fernandez-Luque L, Karlsen R, Melton GB. HealthTrust: A Social Network Approach for Retrieving Online Health Videos. J Med Internet Res 2012; 14 (01) e22
    CrossrefPubMedGoogle Scholar
  • 41 Fernandez-Luque L, Karlsen R, Genevieve B Melton. HealthTrust: Trust-based Retrieval of YouTube’s Diabetes Channels. Proceedings of the 20th ACM Conference on Information and Knowledge Management; 2011 Oct 24–28. Glasgow, Scotland, UK: 2011: 1917-1920.
    Google Scholar
  • 42 Konstantinidis ST, Fernandez-Luque L, Bamidis PD, Karlsen R. Exploring Social Media and Semantic Web for Health Education. Proceedings of the 2nd International Workshop on Web Science and Information Exchange in the Medical Web (MedEx 2011), in 20th ACM Conference on Information and Knowledge Management; 2011 Oct 24–28. Glaskow, UK: 2011: 18-19.
    Google Scholar
  • 43 Mitsopoulou E, Taibi D, Giordano D, Dietze S, Yu HQ Bamidis. et al Connecting medical educational resources to the Linked Data cloud: the mEducator RDF Schema, store and API. Proceeding of the 1st International Workshop on eLearning Approaches for the Linked Data Age within 8th Extended Semantic Web Conference (ESWC2011); 2011 May 29. Heraklion, Greece: 2011.
    Google Scholar
  • 44 BioPortal REST services Wiki. [cited 2012 Jun 7]. Available from. http://www.bioontology.org/wiki/index.php/BioPortal_REST_services.
    PubMedGoogle Scholar
  • 45 Bamidis PD, Konstantinidis ST, Bratsas C, Iyengar M S. Federating Learning Management Systems for Medical Education: a persuasive technologies perspective. Proceedings of the 24th IEEE International Symposium on COMPUTER-BASED MEDICAL SYSTEMS (CBMS 2010);2011 June 27–30. University of the West of England, Bristol, UK: 2011.
    Google Scholar
  • 46 mEducator consortium. D.7.1 - User Guides and Technical Manuals. Deliverable of mEducator project. 2012 (cited 2012 Feb 28). Available from. http://www.meducator.net/?q=content/d71-core-documentation.
    PubMedGoogle Scholar
  • 47 Paraskakis I. Promoting Cross Language communication in MORMED, a Multilingual Social Networking Platform for the Lupus disease. In Bamidis P. et al editors E-education E-science. Plovdiv, Bulgaria: Medical Publishing VAP; 2011. (ISBN 978–960–243–682–0)
    Google Scholar
  • 48 O’Grady L, Wathen CN, Charnaw-Burger J, Betel L, Shachak A, Luke R. etal The use of tags and tag clouds to discern credible content in online health message forums. Int J Med Inform 2012; 81 (01) 36-44.
    CrossrefPubMedGoogle Scholar
  • 49 Fernandez-Luque L, Elahi N, Grajales FJ. An analysis of personal medical information disclosed in YouTube videos created by patients with multiple sclerosis. Stud Health Technol Inform 2009; 150: 292-296.
    PubMedGoogle Scholar