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Neural Networks in Clinical Medicine

Author

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  • Will Penny
  • David Frost

Abstract

Neural networks are parallel, distributed, adaptive information-processing systems that develop their functionality in response to exposure to information. This paper is a tutorial for researchers intending to use neural nets for medical decision-making applications. It includes detailed discussion of the issues particularly relevant to medical data as well as wider issues relevant to any neural net application. The article is restricted to back-propagation learning in multilayer perceptrons, as this is the neural net model most widely used in medical applications. Key words: neural networks; medical decision making; pattern recognition; nonlinearity; error back-propagation; multi layer perceptron. (Med Decis Making 1996;16:386-398)

Suggested Citation

  • Will Penny & David Frost, 1996. "Neural Networks in Clinical Medicine," Medical Decision Making, , vol. 16(4), pages 386-398, October.
    • Handle: RePEc:sae:medema:v:16:y:1996:i:4:p:386-398
    DOI: 10.1177/0272989X9601600409
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    Cited by:

    1. Nida Shahid & Tim Rappon & Whitney Berta, 2019. "Applications of artificial neural networks in health care organizational decision-making: A scoping review," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
    2. Paul S. Heckerling & Ben S. Gerber & Thomas G. Tape & Robert S. Wigton, 2003. "Prediction of Community-Acquired Pneumonia Using Artificial Neural Networks," Medical Decision Making, , vol. 23(2), pages 112-121, March.

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