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A Probabilistic Graphical Model for Tuning Cochlear Implants

  • Iñigo Bermejo
  • Francisco Javier Díez
  • Paul Govaerts
  • Bart Vaerenberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7885)

Abstract

Severe and profound hearing losses can be treated with cochlear implants (CI). Given that a CI may have up to 150 tunable parameters, adjusting them is a highly complex task. For this reason, we decided to build a decision support system based on a new type of probabilistic graphical model (PGM) that we call tuning networks. Given the results of a set of audiological tests and the current status of the parameter set, the system looks for the set of changes in the parameters of the CI that will lead to the biggest improvement in the user’s hearing ability. Because of the high number of variables involved in the problem we have used an object-oriented approach to build the network. The prototype has been informally evaluated comparing its advice with those of the expert and of a previous decision support system based on deterministic rules. Tuning networks can be used to adjust other electrical or mechanical devices, not only in medicine.

Keywords

Bayesian Network Tunable Parameter Cochlear Implant Decision Node Probabilistic Graphical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Govaerts, P.J., Vaerenberg, B., Ceulaer, G.D., Daemers, K., Beukelaer, C.D., Schauwers, K.: Development of a software tool using deterministic logic for the optimization of cochlear implant processor programming. Otology & Neurology 31, 908–918 (2010)CrossRefGoogle Scholar
  2. 2.
    Szlavik, Z., Vaerenberg, B., Kowalczyk, W., Govaerts, P.: Opti-fox: towards the automatic tuning of cochlear implants. In: Proceedings of the 20th Belgian Dutch Conference on Machine Learning, pp. 79–80 (2011)Google Scholar
  3. 3.
    Heckerman, D.: Causal independence for knowledge acquisition and inference. In: Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence (UAI 1993), Washington, D.C, pp. 122–127. Morgan Kaufmann, San Mateo (1993)Google Scholar
  4. 4.
    Heckerman, D., Breese, J.S.: Causal independence for probability assessment and inference using Bayesian networks. IEEE Transactions on Systems, Man and Cybernetics—Part A: Systems and Humans 26, 826–831 (1996)CrossRefGoogle Scholar
  5. 5.
    Díez, F.J., Druzdzel, M.J.: Canonical probabilistic models for knowledge engineering. Technical Report CISIAD-06-01, UNED, Madrid, Spain (2006)Google Scholar
  6. 6.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)Google Scholar
  7. 7.
    Howard, R.A., Matheson, J.E.: Influence diagrams. In: Howard, R.A., Matheson, J.E. (eds.) Readings on the Principles and Applications of Decision Analysis, pp. 719–762. Strategic Decisions Group, Menlo Park (1984)Google Scholar
  8. 8.
    Koller, D., Pfeffer, A.: Object-oriented Bayesian networks. In: Proceedings of the Thirteenth Conference in Artificial Intelligence (UAI 1997), pp. 302–313. Morgan Kaufmann, San Francisco (1997)Google Scholar
  9. 9.
    Bangsø, O., Wuillemin, P.H.: Top-down construction and repetetive structures representation in Bayesian networks. In: Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2000), Orlando, FL, pp. 282–286 (2000)Google Scholar
  10. 10.
    Shachter, R., Peot, M.: Simulation approaches to general probabilistic inference on belief networks. In: Henrion, M., Shachter, R.D., Kanal, L.N., Lemmer, J.F. (eds.) Uncertainty in Artificial Intelligence 5, pp. 221–231. Elsevier Science Publishers, Amsterdam (1990)Google Scholar
  11. 11.
    Lauritzen, S.L.: The EM algorithm for graphical association models with missing data. Comput. Stat. Data Anal. 19(2), 191–201 (1995)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Iñigo Bermejo
    • 1
  • Francisco Javier Díez
    • 1
  • Paul Govaerts
    • 2
  • Bart Vaerenberg
    • 2
    • 3
  1. 1.ETSI InformáticaUNEDMadridSpain
  2. 2.The EargroupAntwerp-DeurneBelgium
  3. 3.Laboratory of Biomedical PhysicsUniversity of AntwerpBelgium

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