Decision support system for the diagnosis of schizophrenia disorders

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dc.contributor.author Razzouk, Denise [UNIFESP]
dc.contributor.author Mari, Jair de Jesus [UNIFESP]
dc.contributor.author Shirakawa, Itiro [UNIFESP]
dc.contributor.author Wainer, Jacques [UNIFESP]
dc.contributor.author Sigulem, Daniel [UNIFESP]
dc.date.accessioned 2015-06-14T13:31:55Z
dc.date.available 2015-06-14T13:31:55Z
dc.date.issued 2006-01-01
dc.identifier http://dx.doi.org/10.1590/S0100-879X2006000100014
dc.identifier.citation Brazilian Journal of Medical and Biological Research. Associação Brasileira de Divulgação Científica, v. 39, n. 1, p. 119-128, 2006.
dc.identifier.issn 0100-879X
dc.identifier.uri http://repositorio.unifesp.br/handle/11600/2872
dc.description.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. en
dc.format.extent 119-128
dc.language.iso eng
dc.publisher Associação Brasileira de Divulgação Científica
dc.relation.ispartof Brazilian Journal of Medical and Biological Research
dc.rights Acesso aberto
dc.subject Clinical decision support systems en
dc.subject Artificial intelligence en
dc.subject Decision making en
dc.subject Expert systems en
dc.subject Schizophrenia en
dc.subject Medical informatics en
dc.title Decision support system for the diagnosis of schizophrenia disorders en
dc.type Artigo
dc.contributor.institution Universidade Federal de São Paulo (UNIFESP)
dc.description.affiliation Universidade Federal de São Paulo (UNIFESP) Escola Paulista de Medicina Departamento de Psiquiatria
dc.description.affiliation Universidade Federal de São Paulo (UNIFESP) Escola Paulista de Medicina Departamento de Informática Médica
dc.description.affiliationUnifesp UNIFESP, EPM, Depto. de Psiquiatria
dc.description.affiliationUnifesp UNIFESP, EPM, Depto. de Informática Médica
dc.identifier.file S0100-879X2006000100014.pdf
dc.identifier.scielo S0100-879X2006000100014
dc.identifier.doi 10.1590/S0100-879X2006000100014
dc.description.source SciELO
dc.identifier.wos WOS:000235089500014



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