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Improved statistical classification methods in computerized psychiatric diagnosis
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DOI：10.1177/0272989X0002000112.png)
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Background. Mainstream psychiatric diagnosis involves mainly sequential, expert-system-derived, logical decision rules. Among the few statistical classification methods that have been sporadically evaluated are Bayes, k-nearest neighbor, and discriminant analysis classifiers. Methods. A statistical classification method based on artificial neural networks (ANN) with task-specific constrained architectures was applied to a sample of 796 clinical interviews, where the symptom evaluation and the diagnostic judgments were made using the Psychiatric State Examination (PSE) system. The proposed constrained ANN (CANN) method was compared with other statistical classification methods. Results. CANN was found to be superior to all other considered methods, having an overall correct classification rate of 80% when applied to test data. Similarly, the concordance coefficients of agreement with the PSE diagnostic categories were all very high. Among the other used methods, discriminant analysis had slightly inferior performance but better generalization capability. Conclusions. The proposed CANN method has a definite utility in psychiatric diagnosis and requires further evaluation, perhaps alongside other standard classification systems and/or with larger samples.
Keyword:
computer-aided psychiatric diagnosis
artificial neural networks
expert systems
classification methods
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