Clinical Orthopaedics and Related Research®

, Volume 468, Issue 10, pp 2612–2620 | Cite as

Computational Ontologies in Orthopaedic Surgery

  • Ricardo Pietrobon
  • Amrapali Zaveri
  • Luciana Cofiel
  • Jacson Barros
  • Jatin Shah
Symposium: ABJS Carl T. Brighton Workshop on Health Informatics

Abstract

Background

Information Technology (IT) plays an important role in storing and collating the vast amounts of healthcare data. However, analyzing and integrating this data to extract useful information is difficult due to the heterogeneous, siloed, disparate, and unstructured nature of the data.

Where are we now?

Attempts to standardize data reporting by establishing reporting standards, checklists and guidelines have not been optimal [3, 11, 19]. Moreover, efforts to integrate data through the use of registries, data sharing networks, vocabularies and data standards have also yielded limited results. These efforts, when applied to orthopaedics, where theoretical knowledge is scattered over subspecialties, make it a cognitively challenging and tedious process.

Where do we need to go?

Implementing data standardization is an important step towards homogenizing the data so that it can be integrated. Once integrated, the next step would be data analysis for information extraction. This information would be useful in answering important questions, especially in orthopaedic clinical practice and research, and could even help optimize methodologies in the education field.

How do we get there?

With the ability to describe concepts in a standardized manner and define existing interrelationships, ontologies are a potential solution. They assist in standardizing and integrating data and also impart strong inferential capabilities at a granular level. When applied to orthopaedics, they can standardize data collection, link data sources, generate knowledge based on the assumptions present in the interlinked data, thus answering important questions regarding orthopaedic clinical practice, research and education [22, 28, 30].

References

  1. 1.
    Antezana E, Juiper M, Mironov V. Biological knowledge management: the emerging role of the Semantic Web technologies. Brief Bioinform. 2009;10:392–407.CrossRefPubMedGoogle Scholar
  2. 2.
    Becker M, Heine C, Herrler R, Krempels KH. 2002. OntHoS—an Ontology for Hospital Scenarios. Technical Report No. 300, Julius-MaximiliansUniversit at Wurzburg, Institut fur Informatik; September 2002.Google Scholar
  3. 3.
    Bossuyt PM. The quality of reporting in diagnostic test research: getting better, still not optimal. Clin Chem. 2004;50:465–466.CrossRefPubMedGoogle Scholar
  4. 4.
    Chassin MR, Galvin RW. The urgent need to improve health care quality. JAMA. 1998;280:1000–1005.CrossRefPubMedGoogle Scholar
  5. 5.
    Clarke M. The QUORUM statement. Lancet. 2000;355:756–757.CrossRefPubMedGoogle Scholar
  6. 6.
    Cook C, Hannley M, Richardson JK, Michon J, Harker M, Pietrobon R. Real-time updates of meta-analyses of HIV treatments supported by a biomedical ontology. Account Res. 2007;14:1–18.PubMedGoogle Scholar
  7. 7.
    Correndo G, Alani H. Collaborative support for community data sharing. The 2nd Workshop on Collective Intelligence in Semantic Web and Social Networks. 2008;3:17–20.Google Scholar
  8. 8.
    Dao TT, Marin F, Tho MC. Ontology-based computer-aided decision system: a new architecture and application concerning the musculoskeletal system of the lower limbs. 4th European Conference of the International Federation for Medical and Biological Engineering (ECIFMBE). 2009;22:1540–1543.Google Scholar
  9. 9.
    Echarte F, Astrain JJ, Córdoba A, Villadangos J. Ontology of folksonomy: a new modeling method. In: Proceedings of Semantic Authoring, Annotation and Knowledge Markup. Available at: http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-289/p08.pdf. Accessed April 22, 2010.
  10. 10.
    Echarte F, Astrain JJ, Cordoba A, Villadangos J. Self-adaptation of ontologies to folksonomies in semantic web. Proc World Acad Sci Eng Tech. 2008;33:335–341.Google Scholar
  11. 11.
    Fung AE, Palanki R, Bakri SJ, Depperschmidt E, Gibson A. Applying the CONSORT and STROBE statements to evaluate the reporting quality of neovascular age-related macular degeneration studies. Ophthalmology. 2009;116:286–296.CrossRefPubMedGoogle Scholar
  12. 12.
    Gilbert L, Wills G, Sim YW, Wang C, Stenning M. e-Learning within a Collaborative Orthopaedic Research Environment (CORE). In: Association for Learning Technology (ALT-C 2005). 2005;79–80.Google Scholar
  13. 13.
    Grethe JS. NeuroLex and The Neuroscience Information Framework: Building comprehensive neuroscience ontologies with and for the community. Frontiers in Neuroinformatics. Conference Abstract: 2nd INCF Congress of Neuroinformatics. Available at: http://frontiersin.org/conferences/individual_abstract_listing.php?conferid=155&pap=2535&ind_abs=1&pg=5. Accessed April 22, 2010.
  14. 14.
    Gruber T. Toward principles for the design of ontologies used for knowledge sharing. Int J Hum Comput Stud. 1995;43:907–928.CrossRefGoogle Scholar
  15. 15.
    Gupta A, Bug W, Marenco L, Qian X, Condit C, Rangarajan A, Muller HM, Miller PL, Sanders B, Grethe JS, Astakhov V, Shepherd G, Sternberg PW, Martone ME. Federated access to heterogeneous information resources in the Neuroscience Information Framework (NIF). Neuroinformatics. 2008;6:205–217.CrossRefPubMedGoogle Scholar
  16. 16.
    Han C, Kwakac KP, Marksa DM, Paead CU, Wua LT, Bhatiaa KS, Masanda PS, Patkar AA. The impact of the CONSORT statement on reporting of randomized clinical trials in psychiatry. Contemp Clin Trials. 2008;30:116–122.CrossRefPubMedGoogle Scholar
  17. 17.
    Jarlais DC, Lyles C, Crepaz N, TREND Group. Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: The TREND Statement. Public Health. 2004;94:361–366.CrossRefGoogle Scholar
  18. 18.
    Jonquet C, Musen MA, Shah NH. A system for ontology-based annotation of biomedical data. International Workshop on Data Integration in the Life Sciences. 2008;5109:144–152.CrossRefGoogle Scholar
  19. 19.
    Kane RL, Wang J, Garrard J. Reporting in randomized clinical trials improved after adoption of the CONSORT statement. J Clin Epidemiol. 2007;60:241–249.CrossRefPubMedGoogle Scholar
  20. 20.
    Katsouyanni K. Collaborative research: accomplishments & potential. Environ Health. 2008;7:3.CrossRefPubMedGoogle Scholar
  21. 21.
    Lambert D. Integrating services for integrated medicine: building virtual physiomes with semantics services. Knowledge Media Institute, Open University, Milton Keynes, United Kingdom. 2007. Available at: http://people.kmi.open.ac.uk/dave/publications/lambert-semubicare-2007.pdf. Accessed April 22, 2010.
  22. 22.
    Luengo V, Vadcard L. Design of adaptive feedback in a web educational system. In Proceedings of 12th International Conference on Artificial Intelligence in Education, Workshop on Adaptive Systems for Web-Based Education: Tools and Reusability. 2005.Google Scholar
  23. 23.
    Mathes A. Folksonomies—Cooperative Classification and Communication Through Shared Metadata. 2004. Available at: http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html. Accessed April 22, 2010.
  24. 24.
    Moher D, Schulz KF, Altman DG. The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomized trials. Lancet. 2001;357:1191–1194.CrossRefPubMedGoogle Scholar
  25. 25.
    Ohno–Machado L, Gennari JH, Murphy SN, Jain NL, Tu SW, Oliver DE, Pattison GE, Greenes RA, Shortliffe EH, Barnett GO. The GuideLine interchange format: a model for representing guidelines. J Am Med Inform Assoc. 1998;5:357–372.PubMedGoogle Scholar
  26. 26.
    Peleg M, Wang D, Fodor A, Keren S, Karnieli E. Lessons learned from adapting a generic narrative diabetic-foot guideline to an institutional decision-support system. Stud Health Technol Inform. 2008;139:243–252.PubMedGoogle Scholar
  27. 27.
    Piehl JH, Green S, Silagy C. Training practitioners in preparing systematic reviews: a cross-sectional survey of participants in the Australasian Cochrane Centre training program. BMC Health Serv Res. 2002;2:11.CrossRefPubMedGoogle Scholar
  28. 28.
    Pundt H, Bishr U. Domain ontologies for data sharing–an example from environmental monitoring using field GIS. Computers & Geosciences. 2002;28:95–102.CrossRefGoogle Scholar
  29. 29.
    Rector AL. Compositional models of medical concepts: towards re-usable application-independent medical terminologies. In: Barahona P, Christensen JP, eds. Knowledge and Decisions in Health Telematics. Amsterdam, The Netherlands: IOS Press; 1994:133–142.Google Scholar
  30. 30.
    Rector A, Rogers J, Pole P. The GALEN High Level Ontology. In: Proceedings of the 14th International Congress of the European Federation for Medical Informatics. 1996;174–178.Google Scholar
  31. 31.
    Spiteri L. Controlled Vocabularies and Folksonomies. Presentation at Canadian Metadata Forum. Ottawa, Ontario; 2005. Available at: http://www.collectionscanada.gc.ca/obj/014005/f2/014005-05209-e-e.pdf. Accessed April 22, 2010.
  32. 32.
    Tricco AC, Brehaut J, Chen MG, Moher D. Following 411 Cochrane protocols to completion: a retrospective cohort study. PLoS ONE. 2008;3:e3684.CrossRefPubMedGoogle Scholar
  33. 33.
    Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The strengthening of the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344–349.CrossRefGoogle Scholar
  34. 34.
    Wilczynski NL. Quality of reporting of diagnostic accuracy studies: no change since STARD statement publication–before-and-after study. Radiology. 2008;248:817–823.CrossRefPubMedGoogle Scholar
  35. 35.
    Wills G, Woukeu A, Bailey C, Ong A, Carr L, Conole G, Hall W, Grange S. Ontological Driven Learning Agreements. In: EDMEDIA 2004—World Conference on Education Multimedia, Hypermedia and Telecommunications, June 21–26, 2004, Lugano, Switzerland.Google Scholar

Copyright information

© The Association of Bone and Joint Surgeons® 2010

Authors and Affiliations

  • Ricardo Pietrobon
    • 1
  • Amrapali Zaveri
    • 1
    • 2
  • Luciana Cofiel
    • 1
  • Jacson Barros
    • 1
  • Jatin Shah
    • 3
  1. 1.Department of Surgery, Research on Research GroupDuke University Medical CenterDurhamUSA
  2. 2.National Neuroscience InstituteSingaporeSingapore
  3. 3.Duke-NUS Graduate Medical SchoolSingaporeSingapore

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