Abstract
IntroductionArtificial intelligence algorithms are increasingly used to highlight refined qualifiers of pathologies and to build treatment protocols based on them. These possibilities open up new perspectives for personalized interventions in psychotherapy. The affective neurosciences that link psychopathological phenomena to the hypersensitization of emotional systems are an excellent field of application of deep learning algorithmsObjectivesIn this contribution we present the standardization of a psychodiagnostic test that can be analyzed with a deep learning algorithm for the development of personalized treatments for depressive disorders in a perspective of precision psychotherapyMethodsPreviously we have constructed a psychodiagnostic test that correlates the psychopathological characteristics to the emotional systems described in affective neuroscience. The construction of this test was carried out with the use of a neural network that discriminated 161 items from a pull of 300 psychopathological and character descriptions. In the present work, the 161 selected items were compared, in a sample of 600 subjects, with the measurement of sadness described in the Panksepp model. Comparation was performed with linear and non-linear statistical analysis methods.ResultsThe items emerging from the statistical analyzes as strongly indicative of a hypersensitivity of the sadness system outline a psychopathological profile for which it is possible to adapt specific psychotherapeutic treatment protocols.ConclusionsIn future prospect, neurobiological and psychophysiological variables such as heart rate variability, skin conductance and activity of the areas of the cortex, measured with a scanner of the near infrared photons, will be correlated to these descriptors of psychopathology.
Highlights
Transitions in mental health, such as the onset or sudden progression of psychopathology, are difficult to foresee
If mental health behaves like other complex systems, drops in mental health may be anticipated by early warning signals (EWS), which manifest in the dynamics of time series data
EWS were found for 59% of individuals with a drop in mental health, and for 47% without such a drop
Summary
In the context of COVID-19 pandemic, first-line responders (FLR) are exposed to multiple stress factors, ranging from lack of adequate protective equipment to worries about family health due to work-related exposure to the new coronavirus. Objectives: To explore the literature in order to find evidencebased prevention strategies for PSRD in FLR, strategies resulted from other epidemiological crisis situations (MERS-CoV, H1N1, SARS-CoV) that may be applied in the current pandemic. Evidence-based recommendations for PSRD prevention are lacking, and only general advices have been detected. These suggestions were clustered on institutional level (e.g., involving of medical personnel in administrative decisions, encouraging personal initiatives, longer pauses between shifts) and individual level (e.g., training of coping abilities, relaxation techniques, and peer-focused group support). Conclusions: The need to elaborate guidelines for prevention of PSRD in FLR can not be overemphasized, especially in the pandemic period, in order to avoid the onset of stress-related complications, and to preserve a good quality of the medical activity
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