Decision support system for the diagnosis of schizophrenia disorders

• Main Author: Razzouk, Denise [UNIFESP]
• Publication Date: 2006
• Other Authors: Mari, Jair de Jesus [UNIFESP], Shirakawa, Itiro [UNIFESP], Wainer, Jacques [UNIFESP], Sigulem, Daniel [UNIFESP]
• Format: Article
• Language: eng
• Source: Repositório Institucional da UNIFESP
• dARK ID: ark:/48912/001300002t4sb
• Download full: http://dx.doi.org/10.1590/S0100-879X2006000100014; http://repositorio.unifesp.br/handle/11600/2872
• topic: Clinical decision support systems; Artificial intelligence; Decision making; Expert systems; Schizophrenia; Medical informatics

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.