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Intelligent Engineering Systems through Artificial Neural Networks, Volume 16

Editor
Cihan H. Dagli
Cihan H. Dagli
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Anna L. Buczak
Anna L. Buczak
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David L. Enke
David L. Enke
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Mark Embrechts
Mark Embrechts
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Okan Ersoy
Okan Ersoy
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ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006

Advances in datamining have allowed for the development a new class of diagnostic tools for use by clinical practitioners in critical care situations. These tools enhance the clinician's diagnostic expertise by providing additional information based on a specific case being evaluated. This paper presents a comparison of the diagnostic capabilities in three situations; the clinician's assessment, the diagnostic tool's assessment, and the assessment made by combining the clinician's diagnosis with the information contained in tool. The tool, a Bayesian Network generated using a Genetic Algorithm based search framework, utilized a dataset created from initial case data collected in an emergency room setting. The results indicate that while the clinician out performs the tool in ruling out ACS, there is a significant improvement overall when the clinician's assessment is added to the tool's input.

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