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The Use of Bayesian Networks in Decision-Making

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Key Topics in Surgical Research and Methodology

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Abstract

Bayesian networks (BNs) are graphical tools of reasoning with uncertainties. In recent years, BNs have been increasingly recognized for their capacity to represent probabilistic dependencies explicitly and intuitively, handle incomplete information, and capture expert judgments along with hard data. In this chapter, we examine the underlying logic of BNs and discuss their applications.

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Notes

  1. 1.

    We constructed all the Bayesian networks in this chapter using Netica® (Norsys Software Corp. http://www.norsys.com/).

  2. 2.

    If the interpretation has a fixed relationship with the actual test, e.g., correct 80% of the time regardless of the test results, then we can keep the original network (Fig. 28.1), and simply provide evidences in probability terms, e.g., P(X-ray result = negative) = 0.80. The expanded network (Fig. 28.2) can handle more complex relationships, such as when the accuracy of the interpretation differs for a positive and a negative test result.

Abbreviations

AUC:

Area under the ROC curve

BN:

Bayesian network

NPV:

Negative predictive value

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

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Authors and Affiliations

  1. The Department of Biosurgery and Surgical Technology, Imperial College London, 10th Floor, Queen Elizabeth the Queen Mother (QEQM) Building, Imperial College Healthcare NHS Trust at St Mary's Hospital Campus, Praed Street, London, W2 1NY, UK

    Zhifang Ni & George B. Hanna

  2. The Department of Management, London School of Economics and Political Science, Houghton Street, London, WC2A 2AE, UK

    Lawrence D. Phillips

Authors
  1. Zhifang Ni
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  2. Lawrence D. Phillips
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  3. George B. Hanna
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Editor information

Editors and Affiliations

  1. Dept. Biosurgery & Surgical Technology, Imperial College London St. Mary's Hospital London, 10th floor QEQM Bldg. Praed Street, London, W2 1NY, United Kingdom

    Thanos Athanasiou  & Ara Darzi  & 

  2. UCSF Global Health Sciences, 3333 California Street, Suite 285, San Francisco L, CA, 94143-0443, USA

    Haile Debas

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Ni, Z., Phillips, L.D., Hanna, G.B. (2010). The Use of Bayesian Networks in Decision-Making. In: Athanasiou, T., Debas, H., Darzi, A. (eds) Key Topics in Surgical Research and Methodology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71915-1_28

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  • DOI: https://doi.org/10.1007/978-3-540-71915-1_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71914-4

  • Online ISBN: 978-3-540-71915-1

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