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(Reference retrieved automatically from SciELO through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Decision support system for the diagnosis of schizophrenia disorders

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Author(s):
D. Razzouk [1] ; J.J. Mari [2] ; I. Shirakawa [3] ; J. Wainer [4] ; D. Sigulem [5]
Total Authors: 5
Affiliation:
[1] Universidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Psiquiatria
[2] Universidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Psiquiatria
[3] Universidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Psiquiatria
[4] Universidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Informática Médica - Brasil
[5] Universidade Federal de São Paulo. Escola Paulista de Medicina. Departamento de Informática Médica - Brasil
Total Affiliations: 5
Document type: Journal article
Source: Brazilian Journal of Medical and Biological Research; v. 39, n. 1, p. 119-128, 2006-01-00.
Abstract

Clinical decision support systems are useful tools for assisting physicians to diagnose complex illnesses. Schizophrenia is a complex, heterogeneous and incapacitating mental disorder that should be detected as early as possible to avoid a most serious outcome. These artificial intelligence systems might be useful in the early detection of schizophrenia disorder. The objective of the present study was to describe the development of such a clinical decision support system for the diagnosis of schizophrenia spectrum disorders (SADDESQ). The development of this system is described in four stages: knowledge acquisition, knowledge organization, the development of a computer-assisted model, and the evaluation of the system's performance. The knowledge was extracted from an expert through open interviews. These interviews aimed to explore the expert's diagnostic decision-making process for the diagnosis of schizophrenia. A graph methodology was employed to identify the elements involved in the reasoning process. Knowledge was first organized and modeled by means of algorithms and then transferred to a computational model created by the covering approach. The performance assessment involved the comparison of the diagnoses of 38 clinical vignettes between an expert and the SADDESQ. The results showed a relatively low rate of misclassification (18-34%) and a good performance by SADDESQ in the diagnosis of schizophrenia, with an accuracy of 66-82%. The accuracy was higher when schizophreniform disorder was considered as the presence of schizophrenia disorder. Although these results are preliminary, the SADDESQ has exhibited a satisfactory performance, which needs to be further evaluated within a clinical setting. (AU)