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The British Journal of Psychiatry 169: 64-67 (1996)
© 1996 The Royal College of Psychiatrists

Artificial neural network to assist psychiatric diagnosis

Y Zou, Y Shen, L Shu, Y Wang, F Feng, K Xu, Y Ou, Y Song, Y Zhong, M Wang and W Liu
Washington Institute, University of Washington, Tacroma 98498-7213, USA. zou@u.washington.edu

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.


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G. E. Davis, W. E. Lowell, and G. L. Davis
Determining the Number of State Psychiatric Hospital Beds by Measuring Quality of Care with Artificial Neural Networks
American Journal of Medical Quality, March 1, 1998; 13(1): 13 - 24.
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