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Evidence Synthesis Using Bayesian Belief Networks

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Evidence Synthesis in Healthcare

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Abstract

Bayesian belief networks (BBNs) are graphical tools for reasoning with uncertainties. In BBNs, uncertain events are represented as nodes and their relationships as links, with missing links indicating conditional independence. BBNs perform belief updating when new information becomes available; they can handle incomplete information and capture expert judgments along with data. BBNs provide a normative framework for synthesizing uncertain evidence.

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Abbreviations

BBN:

Bayesian belief network

NPV:

Negative predictive value

PPV:

Positive predictive value

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Acknowledgement

The authors wish to thank Norman Fenton and William Marsh for insightful discussions on Bayesian networks.

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Authors and Affiliations

  1. Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital Campus, London, UK

    Zhifang Ni

Authors
  1. Zhifang Ni
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  2. Lawrence D. Phillips
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  3. George B. Hanna
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Corresponding author

Correspondence to Zhifang Ni .

Editor information

Editors and Affiliations

  1. St. Mary' Hospital London, Dept. Biosurgery & Surgical Technology, Imperial College London, London, W2 1NY, United Kingdom

    Ara Darzi

  2. St. Mary' Hospital London, Dept. Biosurgery & Surgical Technology, Imperial College London, London, W2 1NY, United Kingdom

    Thanos Athanasiou

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© 2011 Springer London

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Ni, Z., Phillips, L.D., Hanna, G.B. (2011). Evidence Synthesis Using Bayesian Belief Networks. In: Darzi, A., Athanasiou, T. (eds) Evidence Synthesis in Healthcare. Springer, London. https://doi.org/10.1007/978-0-85729-206-3_7

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  • DOI: https://doi.org/10.1007/978-0-85729-206-3_7

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  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-175-2

  • Online ISBN: 978-0-85729-206-3

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