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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20
ISBN:
9780791859599
No. of Pages:
686
Publisher:
ASME Press
Publication date:
2010
eBook Chapter
39 Artificial Neural Network-Based Classification of Medical Students' Disease Diagnosis Capability
By
S. Chakrabarti
,
S. Chakrabarti
School of Engineering
The University of Kansas
1520 West 15th Street Lawrence, KS-66045
; chakra@eecs.ku.edu
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H. Torress
,
H. Torress
School of Engineering
The University of Kansas
1520 West 15th Street Lawrence, KS-66045
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H. Chumley
,
H. Chumley
School of Medicine
The University of Kansas
3901 Rainbow Blvd Mailstop 4010 Kansas City, KS-66160
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J. Delzell
J. Delzell
School of Medicine
The University of Kansas
3901 Rainbow Blvd Mailstop 4010 Kansas City, KS-66160
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Page Count:
8
-
Published:2010
Citation
Chakrabarti, S, Torress, H, Chumley, H, & Delzell, J. "Artificial Neural Network-Based Classification of Medical Students' Disease Diagnosis Capability." Intelligent Engineering Systems through Artificial Neural Networks, Volume 20. Ed. Dagli, CH. ASME Press, 2010.
Download citation file:
Since diagnostic errors can lead to severe consequences for a patient, medical schools are searching for advanced methodologies to teach and asses diagnostic reasoning. Computer software or actors portraying patients are used to present the symptoms of a disease to the students, and the students' approach in diagnosing that disease are recorded. An expert analyzes that record to classify each approach as being correct or incorrect and then recommends appropriate training for each student. With the goal of automating this classification process, neural network-based supervised algorithms are tested in this investigation. The task here is to reliably classify the diagnosis...
Abstract
1. Introduction
2. Procedure
3. Training and testing using standard classifiers
4. Concluding Remarks
Acknowledgements
References
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