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


METHODS
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.


CONCLUSIONS
ANN might be used to improve psychiatric diagnosis.
Zou
Y
Y
Washington Institute, University of Washington, Tacroma 98498-7213, USA. zou@u.washington.edu
Shen
Y
Y
Shu
L
L
Wang
Y
Y
Feng
F
F
Xu
K
K
Ou
Y
Y
Song
Y
Y
Zhong
Y
Y
Wang
M
M
Liu
W
W
D000328:Adult; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D005103:Expert Systems; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D009497:Neurotic Disorders / Q000145:classification / Q000175:diagnosis* / Q000523:psychology; D011569:Psychiatric Status Rating Scales / Q000706:statistics & numerical data; D011594:Psychometrics; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000145:classification / Q000175:diagnosis*; D012565:Schizophrenic Psychology; D012984:Software