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International Conference on Artificial Intelligence: Methodology, Systems, and Applications

AIMSA 2016: Artificial Intelligence: Methodology, Systems, and Applications pp 356–359Cite as

The Select and Test (ST) Algorithm and Drill-Locate-Drill (DLD) Algorithm for Medical Diagnostic Reasoning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9883))

Abstract

Two algorithms for medical diagnostic reasoning along with their knowledgebase design and a method known as orthogonal vector projection for determining differential diagnoses are described. Whilst further research is necessary to achieve an effective full automation of medical diagnostic reasoning, these two algorithms provide the necessary initial theoretical foundations.

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References

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW, Australia

    D. A. Irosh P. Fernando & Frans A. Henskens

  2. Distributed Computing Research Group, University of Newcastle, Callaghan, NSW, Australia

    D. A. Irosh P. Fernando & Frans A. Henskens

  3. School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, Australia

    D. A. Irosh P. Fernando

  4. Health Behaviour Research Group, University of Newcastle, Callaghan, NSW, Australia

    Frans A. Henskens

Authors
  1. D. A. Irosh P. Fernando
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  2. Frans A. Henskens
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Corresponding author

Correspondence to Frans A. Henskens .

Editor information

Editors and Affiliations

  1. Winston-Salem State University, Winston Salem, North Carolina, USA

    Christo Dichev

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

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© 2016 Springer International Publishing Switzerland

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Fernando, D.A.I.P., Henskens, F.A. (2016). The Select and Test (ST) Algorithm and Drill-Locate-Drill (DLD) Algorithm for Medical Diagnostic Reasoning. In: Dichev, C., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science(), vol 9883. Springer, Cham. https://doi.org/10.1007/978-3-319-44748-3_36

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  • DOI: https://doi.org/10.1007/978-3-319-44748-3_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44747-6

  • Online ISBN: 978-3-319-44748-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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