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Disease Models, Part II: Querying & Applications

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Medical Imaging Informatics

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Abstract

In the previous chapter, the mathematical formalisms that allow us to encode medical knowledge into graphical models were described. Here, we focus on how users can interact with these models (specifically, belief networks) to pose a wide range of questions and understand inferred results - an essential part of the healthcare process as patients and healthcare providers make decisions. Two general classes of queries are explored: belief updating, which computes the posterior probability of the network variables in the presence of evidence; and abductive reasoning, which identifies the most probable instantiation of network variables given some evidence. Many diagnostic, prognostic, and therapeutic questions can be represented in terms of these query Types. For models that are complex, exact inference techniques are computationally intractable; instead, approximate inference methods can be leveraged. We also briefly cover special classes of belief networks that are relevant in medicine: probabilistic relational models, which provide a compact representation of large number of propositional variables through the use of first-order logic; influence diagrams, which provide a means of selecting optimal plans given cost/preference constraints; and naïve Bayes classifiers. Importantly, the question of how to validate the accuracy of belief networks is explored through cross validation and sensitivity analysis. Finally, we explore how the intrinsic properties of a graphical model (e.g., variable selection, structure, parameters) can assist users with interacting with and understanding the results of a model through feedback. Applications of Bayesian belief networks in image processing, querying, and case-based retrieval from large imaging repositories are demonstrated.

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Notes

  1. 1.

    As in Chapter 8, we follow standard notation with uppercase letters representing a random variable; lowercase letters indicating instantiations/specific values of the random variable; and bold characters symbolizing sets or vectors of variables.

  2. 2.

    Belief propagation is sometimes also referred to as the sum-product algorithm.

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Authors and Affiliations

  1. Medical Imaging Informatics, UCLA David Geffen School of Medicine, Los Angeles, CA, USA

    William Hsu PhD

  2. Medical Imaging Informatics Group Department of Radiological Sciences, David Geffen School of Medicine University of California, Los Angeles, 924 Westwood Blvd., Suite 420, Los Angeles, CA, 90024, USA

    Alex A. T. Bui

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Correspondence to Alex A. T. Bui .

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  1. Medical Imaging Informatics Group, University of California, Los Angeles, Westwood Blvd. 924 , Los Angeles, 90024, U.S.A.

    Alex A.T. Bui

  2. Medical Imaging Informatics Group, University of California, Los Angeles, Westwood Blvd. 924 , Los Angeles, 90024, U.S.A.

    Ricky K. Taira

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Hsu, W., Bui, A.A.T. (2010). Disease Models, Part II: Querying & Applications. In: Bui, A., Taira, R. (eds) Medical Imaging Informatics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0385-3_9

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  • DOI: https://doi.org/10.1007/978-1-4419-0385-3_9

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