Abstract :
[en] The impact of diagnostic delay in mental health is significant in terms of neurocognitive impairment, comorbidities, prognostic and socio-economical cost. For this reason, diagnostical research in psychiatry and the classification methods are continuously questioned. The network theory of mental disorders aims at contributing to the improvement of psychiatric diagnosis and considers mental disorders as the results of complex sets of interactions among symptoms instead of being their common cause. In this study, we use network theory and its associate statistical methods, namely Gaussian Graphical Models, centrality, and cluster analysis, to estimate respectively the interactions among symptoms from different disorders, their relative importance, and how they overlap, in a sample of psychiatric inpatients. The community detection found nine clusters with their interactions. Many are closely related to DSM criteria but some of them share symptoms from both diagnostics. One central symptom of the construct is Insomnia. There was a significant difference in the sum scores for psychotic symptoms, but not for bipolar symptoms, across psychotic and bipolar patients. This study needs however to be replicated in a bigger sample of different patients. Computing Bayesian Network to assess causalities in the network and adding other variables (such as biomarkers or therapeutic responses) could contribute to a more personalized diagnostic. How symptoms connect to each other in a specific time frame would define a person phenotype. Network analysis allows for investigating connections, identifying which symptoms are relatively important to the self-determination of disorders as well as how network nodes predict each other and arise in communities. For instance, in psychotic and mood disorders, sleep related symptoms or altered speech features and the importance of their communities in the probable transfer of symptomatology.
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