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Artificial Neural Network Models for Timely Assessment of Trauma Complication Risk

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Computational Intelligence Processing in Medical Diagnosis

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 96))

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Abstract

This chapter espouses the deployment of neural network-based diagnostic aids for evaluation of morbidity risks in the prehospital, acute care, and rehabilitation circumstances evinced by traumatic injury. The potential effectiveness of such systems is addressed from several points of view. First, the ability of the underlying connectionist models to identify complex, highly nonlinear, and sometimes even counterintuitive patterns in trauma data is discussed. Prior work in the area is reviewed and the approach is illustrated with an application that succeeds in identifying coagulopathy outcomes in victims of blunt injury trauma. Second, the feasibility of the universal applicability of neural models in actual trauma situations is argued. Their ability to use standardized, widely available data and their capacity for reflecting local differences and changing conditions is exposed. Finally, the potential enhancements for such models are explored in the contexts of clinical decision support systems.

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Authors
  1. R. P. Marble
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  2. J. C. Healy
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Editor information

Editors and Affiliations

  1. Technical University of Munich, Ismaninger Straße 22, 81675, München, Germany

    Manfred Schmitt

  2. Romanian Academy, Calea Victoriei 125, Bucharest, Romania

    Horia-Nicolai Teodorescu

  3. The Queens Elizabeth Hospital, Woodville Road, 5011, Woodville, Adelaide, South Australia, Australia

    Ashlesha Jain

  4. Bellevue Residential Care Centre, 5050, Bellevue Heights, Adelaide, South Australia, Australia

    Ajita Jain

  5. Julia Farr Services, Fullarton Road, 5063, Adelaide, South Australia, Australia

    Sandyha Jain

  6. Knowledge-Based Intelligent, Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, Adelaide, South Australia, Australia

    Lakhmi C. Jain

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© 2002 Springer-Verlag Berlin Heidelberg

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Marble, R.P., Healy, J.C. (2002). Artificial Neural Network Models for Timely Assessment of Trauma Complication Risk. In: Schmitt, M., Teodorescu, HN., Jain, A., Jain, A., Jain, S., Jain, L.C. (eds) Computational Intelligence Processing in Medical Diagnosis. Studies in Fuzziness and Soft Computing, vol 96. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1788-1_7

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  • DOI: https://doi.org/10.1007/978-3-7908-1788-1_7

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2509-1

  • Online ISBN: 978-3-7908-1788-1

  • eBook Packages: Springer Book Archive

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