Decision support system for the diagnosis of schizophrenia disorders

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 computerassisted 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 decisionmaking 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.

Fuzzy Clustering Based Bayesian Framework to Predict Mental Health Problems Among Children

“…Razzouk et al [9] developed a decision support system for the diagnosis of schizophrenia disorders with an accuracy of 66-82%. Chattopadhyay et al [10] developed a neuro-fuzzy approach to grade adult depression.…”

Prediction of Mental Health Problems Among Children Using Machine Learning Techniques

“…The current trend and future applications and their implications have been discussed. Razzouk D., et al [9] developed a decision support system for the diagnosis of schizophrenia disorders with an accuracy of 66-82%. Chattopadhyay S., et al [10] developed a neurofuzzy approach to grade adult depression.…”

Intelligent Decision Support System for Depression Diagnosis Based on Neuro-fuzzy-CBR Hybrid

“…Although the approach greatly reduced the demand on therapist's time, it lacked the patient -doctor feedback mechanism which is crucial for depression therapy. In Razzouk, Mari, Wainer, & Sigulem (2006), a pilot study for modeling the diagnoses of schizophrenia and other psychotic disorders is reported. In Mila et al (2009), a fuzzy semiotic framework for modeling imprecision in the assessment of depression is presented.…”