Decision support system for the diagnosis of schizophrenia disorders

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.

Psychiatric disorders and cardiac anxiety in exercising and sedentary coronary artery disease patients: a case-control study

Regular physical exercise has been shown to favorably influence mood and anxiety; however, there are few studies regarding psychiatric aspects of physically active patients with coronary artery disease (CAD). The objective of the present study was to compare the prevalence of psychiatric disorders and cardiac anxiety in sedentary and exercising CAD patients. A total sample of 119 CAD patients (74 men) were enrolled in a case-control study. The subjects were interviewed to identify psychiatric disorders and responded to the Cardiac Anxiety Questionnaire. In the exercise group (N = 60), there was a lower prevalence (45 vs 81%; P < 0.001) of at least one psychiatric diagnosis, as well as multiple comorbidities, when compared to the sedentary group (N = 59). Considering the Cardiac Anxiety Questionnaire, sedentary patients presented higher scores compared to exercisers (mean ± SEM = 55.8 ± 1.9 vs 37.3 ± 1.6; P < 0.001). In a regression model, to be attending a medically supervised exercise program presented a relevant potential for a 35% reduction in cardiac anxiety. CAD patients regularly attending an exercise program presented less current psychiatric diagnoses and multiple mental-related comorbidities and lower scores of cardiac anxiety. These salutary mental effects add to the already known health benefits of exercise for CAD patients.