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Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
Crowdsourcing As a Means to Identify SNOMED CT Subsets
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Crowdsourcing As a Means to Identify SNOMED CT Subsets

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Dave Parry …

Dave Parry
School of Computing + Mathemtical Sciences, Auckland University of Technology
www.aut.ac.nz
(P12, 1/10/09, Works Room, 5.02)

Published in: Health & Medicine, Technology
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  • 1. “ Crowdsourcing” as a means to identify SNOMED CT subsets – an initial approach Dave Parry School of Computing and Mathematical sciences Auckland University of Technology Dave.parry@aut.ac.nz
  • 2. Agenda
    • Why is coding difficult ?
    • Conceptual Issues
    • What is crowdsourcing ?
    • Structure and software
  • 3. Why is coding difficult ?
    • Experts don’t agree – even when a loose standard of agreement is required (Chiang 2006)
    • SNOMED CT is very large and changes by 5-10% each release
    • Data is used in ways that might be unfamiliar to the originator
    Reliability of SNOMED-CT Coding by Three Physicians using Two Terminology Browsers Michael F. Chiang, John C. Hwang, Alexander C. Yu, Daniel S. Casper, James J. Cimino, and Justin Starren AMIA Annu Symp Proc. 2006; 2006: 131–135.
  • 4. So what ?
    • Errors propagate through systems
      • SNOMED >ICD10 >DRG
    • Free text present in many places in systems.
    • Systems supporting coding may do better in avoiding “Paper trail” errors (O’Malley 2005)
    • O'Malley, K. J., Cook, K. F., Price, M. D., Wildes, K. R., Hurdle, J. F., & Ashton, C. M. (2005). Measuring diagnoses: ICD code accuracy.(International Classification of Diseases). Health Services Research, 40 (5), 1620(1620).
  • 5. Existing systems
    • Patrick et al describe means of selecting the “most likely “ term or phrase.
    • Issues with identifying subsets and confirming correctness
    J. Patrick, Y. Wang, and P. Budd, "An automated system for conversion of clinical notes into SNOMED clinical terminology," in Proceedings of the fifth Australasian symposium on ACSW frontiers - Volume 68 Ballarat, Australia: Australian Computer Society, Inc., 2007.
  • 6. Conceptual basis
    • Although SNOMED CT is hierarchical, there are many relations in addition to IS-A subsumptions.
    • Any hierarchy is based on a particular view of the domain which may not match the reality
  • 7. Concept Concept B Concept Concept Concept A Concept Concept Concept Concept Concept Concepts related to WHU Concepts Unrelated to WHU Concepts partially related to WHU Is-a Is-a Is-a Is-a Is-a To root concepts….
  • 8. 1 Membership value m 0 0 1 2 3 4 5 Value of “relatedness” response Not in subset Fully in subset Partially in subset
  • 9. Women’s health ultrasound
    • Combined radiology and O+ G dept.
    • Diagnostic for both women and fetus
    • Potentially very large subsets
    • Coding important clinically and administratively
  • 10. WHU report
    • “ Growth measurements lie within normal limits for this gestation. Liquor volume is normal. Fetus is active. A single left fetal kidney is identified. No definite right kidney seen in the right renal fossa. Fetal bladder appears normal. “
  • 11. How do we get the membership values ?
    • Via texts – popular but limited
    • People thinking…
      • Lots of work for small numbers
      • Danger of capture by one particular view
      • Hard to get coverage
  • 12. Crowdsourcing
    • Outsourcing to a wide group
      • Anyone who wants to
      • Minimal work
      • “ All of us are smarter than some of us”
  • 13. Examples
    • The GUARDIAN (UK)
    • RECAPTCHA and old texts
    • Common sense computing project
  • 14. System description
    • SQL server database
    • ASP.NET programming
    • SNOMED CT release provided by NZHIS
  • 15. Original text Potential fragments that relate to SNOMED terms Potential SNOMED Concepts Expanded SNOMED Descriptions Selected Concepts Overall scheme
  • 16. Possible Concepts from fragments Original text
  • 17. Rating screen
  • 18. Learning memberships
    • Start system by assigning membership from inspection of hierarchy.
    • Modify membership using responses
    • Present highest ranked member first
  • 19. Plan
    • Collect a number of test cases and use to test software in small unit
    • Test usability of software
    • Launch, with fictional cases to RANZCOG community and others
    • Publish subset data
    • Make system available more widely
  • 20. Discussion
    • There is a lot of data out there.
    • Coding to a wide set of concepts is hard.
    • Confirming that a coding decision is correct or not is easier than selecting a code from a wide range.
    • Coding needs to be part of the workflow
  • 21. Acknowledgements
    • Women’s health ultrasound department at Auckland District Health Board, especially Kathy Dryden, Chief Sonographer.
    • Ted Cizadlo and NZHIS.

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