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Using data mining to Improve Clinical Decision Support

  • Conference paper
Applications and Innovations in Intelligent Systems VII

Abstract

In this paper we report that it is important to train clinical decision support systems on as large a data set as possible. We describe how the diagnosis of heart disease, from 12-lead ECGs, may be improved by up to 32%, with respect to diagnosis using a small data set, by the use of data mining algorithms trained on a sufficiently large data set. We also describe how these suitably trained, and chosen, data mining algorithms appear to be capable of provision of clinical decision support by virtue of the short time, 1-3 seconds, required to perform patient state classification.

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Author information

Authors and Affiliations

  1. Data Mining Group, Department of Computer Science, University of Durham, Science Laboratories, South Road, Durham, Durham, DH1 3LE, UK

    K. E. Burn-Thornton, S. I. Thorpe & J. Attenborough

Authors
  1. K. E. Burn-Thornton
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  2. S. I. Thorpe
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  3. J. Attenborough
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Editor information

Editors and Affiliations

  1. Crew Services Ltd, Fairfield House, Kingston Crescent, Portsmouth, UK

    Richard Ellis BSc, MSc

  2. Department of Accounting and Management Science, Portsmouth Business School, University of Portsmouth, Portsmouth, UK

    Mike Moulton BSc

  3. Department of Computer Science, University of Liverpool, Chadwick Building, Liverpool, UK

    Frans Coenen PhD

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© 2000 Springer-Verlag London

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Burn-Thornton, K.E., Thorpe, S.I., Attenborough, J. (2000). Using data mining to Improve Clinical Decision Support. In: Ellis, R., Moulton, M., Coenen, F. (eds) Applications and Innovations in Intelligent Systems VII. Springer, London. https://doi.org/10.1007/978-1-4471-0465-0_13

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  • DOI: https://doi.org/10.1007/978-1-4471-0465-0_13

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-230-3

  • Online ISBN: 978-1-4471-0465-0

  • eBook Packages: Springer Book Archive

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