Please use this identifier to cite or link to this item: http://repositorio.unifesp.br/handle/11600/2872
Title: Decision support system for the diagnosis of schizophrenia disorders
Authors: Razzouk, Denise [UNIFESP]
Mari, Jair de Jesus [UNIFESP]
Shirakawa, Itiro [UNIFESP]
Wainer, Jacques [UNIFESP]
Sigulem, Daniel [UNIFESP]
Universidade Federal de São Paulo (UNIFESP)
Keywords: Clinical decision support systems
Artificial intelligence
Decision making
Expert systems
Schizophrenia
Medical informatics
Issue Date: 1-Jan-2006
Publisher: Associação Brasileira de Divulgação Científica
Citation: Brazilian Journal of Medical and Biological Research. Associação Brasileira de Divulgação Científica, v. 39, n. 1, p. 119-128, 2006.
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.
URI: http://repositorio.unifesp.br/handle/11600/2872
ISSN: 0100-879X
Other Identifiers: http://dx.doi.org/10.1590/S0100-879X2006000100014
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