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Artificial Neural Network to Assist Psychiatric Diagnosis

Published online by Cambridge University Press:  02 January 2018

Yizhuang Zou ,
Yucun Shen ,
Liang Shu ,
Yufeng Wang ,
Feng Feng ,
Keqin Xu ,
Ying Qu ,
Yanming Song ,
Yixin Zhong  and
Minghui Wang
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Yizhuang Zou*
Affiliation:
Beijing Medical University, Institute of Mental Health, 100083
Yucun Shen
Affiliation:
Beijing Medical University, Institute of Mental Health, 100083
Liang Shu
Affiliation:
Beijing Medical University, Institute of Mental Health, 100083
Yufeng Wang
Affiliation:
Beijing Medical University, Institute of Mental Health, 100083
Feng Feng
Affiliation:
Beijing Huilongguan Hospital, 100085
Keqin Xu
Affiliation:
Beijing Huilongguan Hospital, 100085
Ying Qu
Affiliation:
Beijing Huilongguan Hospital, 100085
Yanming Song
Affiliation:
Beijing Huilongguan Hospital, 100085
Yixin Zhong
Affiliation:
Beijing Posts and Telecommunication University, China, 100088
Minghui Wang
Affiliation:
Beijing Posts and Telecommunication University, China, 100088
Weiquan Liu
Affiliation:
Beijing Posts and Telecommunication University, China, 100088
*
Yizhuang Zou, Washington Institute, University of Washington, 9601 Steilacoom Blvd SW, Tacoma, WA 98498-7213, USA. Fax: (206) 756-3987; e-Mail: zou@u.washington.edu
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Abstract

Background

Artificial Neural Network (ANN), as a potential powerful classifier, was explored to assist psychiatric diagnosis of the Composite International Diagnostic Interview (CIDI).

Method

Both Back-Propagation (BP) and Kohonen networks were developed to fit psychiatric diagnosis and programmed (using 60 cases) to classify neurosis, schizophrenia and normal people. The programmed networks were cross-tested using another 222 cases. All subjects were randomly selected from two mental hospitals in Beijing.

Results

Compared to ICD-10 diagnosis by psychiatrists, the overall kappa of BP network was 0.94 and that of Kohonen was 0.88 (both P < 0.01). In classifying patients who were difficult to diagnose, the kappa of BP was 0.69 (P < 0.01). ANN-assisted CIDI was compared with expert system assisted CIDI (kappa=0.72–0.76); ANN was more powerful than a traditional expert system.

Conclusion

ANN might be used to improve psychiatric diagnosis.

Type
Papers
Information
The British Journal of Psychiatry , Volume 169 , Issue 1 , July 1996 , pp. 64 - 67
Copyright
Copyright © 1996 The Royal College of Psychiatrists 

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References

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Zou, Y., Shen, Y., Shu, L., et al (1994) Research on Methodology of Psychiatric Diagnosis – Artificial Neural Networks and Diagnostic Scales. Dissertation of Beijing Medical University.Google Scholar
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