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Application of Subjective Logic to Health Research Surveys

  • Robert D. Kent
  • Jason McCarrell
  • Gilles Paquette
  • Bryan St. Amour
  • Ziad Kobti
  • Anne W. Snowdon
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 4)

Abstract

The application of semi-automated decision support systems in health care faces challenging tasks, mainly in generating evidence based recommendations in a short critical time window. Traditional data collection and survey methodology to generate evidence for the decision support systems also suffers from a slow turn-around time. In addition to probabilistic Bayesian analysis it is required to support reasoning with uncertainty in the context of the total survey error paradigm. Following Jøsang, subjective logic provides a suitable framework for connecting survey data collection directly to a model of evidence based opinions with uncertainty that also support subjective reasoning. We report on the current design and implementation aspects of a system for application of subjective logic to health research surveys.

Keywords

Subjective logic health care uncertainty survey methodology 

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References

  1. Bates, D.W., Gawande, A.A.: Patient safety: Improving safety with information technology. New England Journal of Medicine 348(25), 2526–2534 (2003)CrossRefGoogle Scholar
  2. Groves, R.M., Fowler, Jr., Floyd, J., Couper, M.P., Lepkowski, J.M., Singer, E., Tourangeau, R.: Survey Methodology. Wiley-Interscience, New Jersey (2004)zbMATHGoogle Scholar
  3. Jøsang, A.: Artificial Reasoning with Subjective Logic. In: Proceedings of the Second Australian Workshop on Commonsense Reasoning, Perth (1997)Google Scholar
  4. Jøsang, A.: A Logic for Uncertain Probabilities. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9(3), 279–311 (2001)MathSciNetGoogle Scholar
  5. Jøsang, A.: The Consensus Operator for Combining Beliefs. Artificial Intelligence Journal 142(1-2), 157–170 (2002)CrossRefGoogle Scholar
  6. Jøsang, A.: Probabilistic Logic Under Uncertainty. In: Proceedings of Computing: The Australian Theory Symposium (CATS 2007), Ballarat (January 2007)Google Scholar
  7. Jøsang, A., McAnally, D.: Multiplication and Comultiplication of Beliefs. International Journal of Approximate Reasoning 38(1), 19–51 (2004)CrossRefGoogle Scholar
  8. Jøsang, A.: Conditional Reasoning with Subjective Logic. Journal of Multiple-Valued Logic and Soft Computing 15(1), 5–38 (2008)Google Scholar
  9. Jøsang, A., Diaz, J., Rifqi, M.: Cumulative and Averaging Fusion of Beliefs. Information Fusion 11(2), 192–200 (2010)CrossRefGoogle Scholar
  10. Jøsang, A., Pope, S., Daniel, M.: Conditional Deduction Under Uncertainty. In: Godo, L. (ed.) ECSQARU 2005. LNCS, vol. 3571, pp. 824–835. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. Jøsang, A., Pope, S., Marsh, S.: Exploring Different Types of Trust Propagation. In: Stølen, K., Winsborough, W.H., Martinelli, F., Massacci, F. (eds.) iTrust 2006. LNCS, vol. 3986, pp. 179–192. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. Kent, R.D., Snowdon, A., Preney, P., Kim, D., Ren, J., Aggarwal, A., Kolga, C., Tiessen, B.: USMS: An open-source IT solution for effective health and safety data collection and decision support. In: Halifax, Ottawa, October 11-13, vol. 7. CPSI: Canadian Patient Safety Institute (2007)Google Scholar
  13. Kent, R.D., Kobti, Z., Snowdon, A., Aggarwal, A.: Towards a Unified Data Management and Decision Support System for Health Care. In: The 3rd International Symposium on Intelligent and Interactive Multimedia: Systems and Services (KES-IIMSS-10), Baltimore, USA, July 28-30 (accepted, 2010)Google Scholar
  14. Kobti, Z., Snowdon, A.W., Kent, R.D., Bhandari, G., Rahaman, S.F., Preney, P.D., Zhu, L., Kolga, C.A., Tiessen, B.: Towards a “Just-in-Time” Distributed Decision Support System in Health Care Research. In: Burstein, F., Brezillon, P., Zaslavsky, A. (eds.) Special Volume in Decision Support Systems: Supporting Real Time Decision-Making: The Role of Context in Decision Support on the Move (in press, 2010)Google Scholar
  15. Krug, E.G., Sharma, G.K., Lozano, R.: The global burden of injuries. American Journal of Public Health 90(4), 523–526 (2000)CrossRefGoogle Scholar
  16. McAnally, D., Jøsang, A.: Addition and Subtraction of Beliefs. In: Proceedings of the conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004), Perugia (July 2004)Google Scholar
  17. Pope, S., Jøsang, A.: Analysis of Competing Hypothesis using Subjective Logic. In: Proceedings of the 10th International Command and Control Research Technology Symposium (ICCRTS 2005), McLean Virginia, USA (2005)Google Scholar
  18. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  19. Shafer, G.: Perspectives on the theory and practice of belief functions. International Journal of Approximate Reasoning 3, 1–40 (1990)Google Scholar
  20. Smets, P., Kennes, R.: The Transferable Belief Model. Artificial Intelligence 66, 191–243 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  21. Smets, P.: Data Fusion in the Transferable Belief Model. In: Proc. 3rd Intl. Conf. Information Fusion, Paris, France, pp. 21–33 (2000)Google Scholar
  22. Snowdon, A., Kent, R., Kobti, Z., Howard, A.: Development of a wireless, web-services based survey for road to measure vehicle safety for Canadian children. In: 8th World Conference on Injury Prevention and Safety Promotion, Durban, South Africa (2006)Google Scholar
  23. Snowdon, A., Howard, A., Boase, P.: The Development of a Protocol for a National Study of Canadian Children’s Safety in Vehicles. In: Canadian Association of Road Safety Professionals, Canadian Multidisciplinary Road Safety Conference Proceedings, Montreal, Quebec (June 2007)Google Scholar
  24. Snowdon, A.W., Hussein, A., Purc-Stevenson, R., Bruce, B., Kolga, C., Boase, P., et al.: Are we there yet? Canada’s progress towards achieving road safety vision 2010 for children travelling in vehicles. International Journal of Injury Control and Safety Promotion (2008)Google Scholar
  25. Tiessen, B., Snowdon, A., Kent, R., Hussein, A., Preney, P., Woolcock, S.: Innovation in Patient Falls: Development of a Wireless Falls Reporting System to Prevent Falls in the Hospitalized Elderly. In: The International Society of Quality in Health Care (ISQual), Copenhagen, Denmark, October 24 (2008)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  • Robert D. Kent
    • 1
  • Jason McCarrell
    • 1
  • Gilles Paquette
    • 1
  • Bryan St. Amour
    • 1
  • Ziad Kobti
    • 1
  • Anne W. Snowdon
    • 1
  1. 1.School of Computer Science and *Odette School of BusinessUniversity of WindsorWindsorCanada

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