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An adaptive two-tier menu approach to support on-line entry of diagnoses

  • Knowledge Acquisition and Learning
  • Conference paper
  • First Online:
Artificial Intelligence in Medicine (AIME 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1211))

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Abstract

We demonstrate how a task model derived from data mining techniques could be used to anticipate diagnosis codes from patient complaints for improved data entry efficiency. The anticipated diagnoses are presented to the user in a two-tier fashion with the first tier presenting the most likely diagnoses based upon the complaints which the second tier refines. A success rate of 70% was obtained in anticipating patient diagnosis.

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References

  1. Canfield, K. (1995) Primary intelligent split menus with text corpora for computerised patient record data-entry. International J. Bio-Medical Computing, Vol. 39, 263–273.

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  3. Mudali, S.K., Warren, J.R., Spenceley, S.E., and Kirkwood, I (1996) Toward an Intelligent Interface for Clinical Data Capture: Probabilistic Analysis of the Subjective Component. Proceedings of the Fourth National Health Informatics Conference (HIC96), B. McGuiness and T. Leeder (eds), Melbourne, August 19–21, 57–62.

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Authors and Affiliations

  1. Advanced Computing Research Centre, School of Computer and Information Science, University of South Australia, 5095, The Levels, SA, Australia

    Sugath K. Mudali, James R. Warren & Susan E. Spenceley

Authors
  1. Sugath K. Mudali
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  2. James R. Warren
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  3. Susan E. Spenceley
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Editor information

Elpida Keravnou Catherine Garbay Robert Baud Jeremy Wyatt

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© 1997 Springer-Verlag Berlin Heidelberg

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Mudali, S.K., Warren, J.R., Spenceley, S.E. (1997). An adaptive two-tier menu approach to support on-line entry of diagnoses. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029445

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  • DOI: https://doi.org/10.1007/BFb0029445

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62709-8

  • Online ISBN: 978-3-540-68448-0

  • eBook Packages: Springer Book Archive

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