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Applications of Probabilistic and Related Logics to Decision Support in Medicine

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Computational Medicine in Data Mining and Modeling

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Abstract

Since the late 60s, probability theory has found application in development of various medical expert systems. Bayesian analysis, which is essentially an optimal path finding through a graph called Bayesian network, has been (and still is) successfully applied in so-called sequential diagnostics, when the large amount of reliable relevant data is available. The graph (network) represents our knowledge about connections between studied medical entities (symptoms, signs, diseases); the Bayes formula is applied in order to find the path (connection) with maximal conditional probability. Moreover, a priori and conditional probabilities were used to define a number of measures designed specifically to handle uncertainty, vague notions, and imprecise knowledge. Some of those measures were implemented in MYCIN in the early 70s [96]. The success of MYCIN has initiated construction of rule-based expert systems in various fields.

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Acknowledgments

We would like to express our gratitude to professor Milan Stošović, MD, for his useful suggestions and comments. This work is partially supported by Serbian Ministry of Education and Science through grants III44006, III41103, ON174062, and TR36001.

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Authors and Affiliations

  1. Faculty of Transportation and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000, Belgrade, Serbia

    Aleksandar Perović

  2. Faculty of Mechanical Engineering, University of Belgrade, Kraljice Marije 16, 11000, Belgrade, Serbia

    Dragan Doder

  3. Mathematical Institute of Serbian Academy of Sciences and Arts, Kneza Mihaila 36, 11000, Belgrade, Serbia

    Zoran Ognjanović

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  1. Aleksandar Perović
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  1. Mathematical Institute, Serbian Academy of Science and Arts, Belgrade, Serbia

    Goran Rakocevic

  2. Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

    Tijana Djukic

  3. Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

    Nenad Filipovic

  4. School of Electrical Engineering, University of Belgrade, Belgrade, Serbia

    Veljko Milutinović

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Perović, A., Doder, D., Ognjanović, Z. (2013). Applications of Probabilistic and Related Logics to Decision Support in Medicine. In: Rakocevic, G., Djukic, T., Filipovic, N., Milutinović, V. (eds) Computational Medicine in Data Mining and Modeling. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8785-2_2

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