Decision support system for the diagnosis of schizophrenia disorders

Autor(es)

D. Razzouk ^[1] ; J.J. Mari ^[2] ; I. Shirakawa ^[3] ; J. Wainer ^[4] ; D. Sigulem ^[5]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: Brazilian Journal of Medical and Biological Research; v. 39, n. 1, p. 119-128, 2006-01-00.

Resumo

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)

• Processo FAPESP: 98/11120-5 - Aquisicao de conhecimento para elaboracao de uma base de conhecimento de um sistema de apoio a decisao no tratamento e diagnostico dos transtornos psicoticos do espectro da esquizofrenia.
• Beneficiário: Jair de Jesus Mari
• Modalidade de apoio: Auxílio à Pesquisa - Regular