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Phenomenological experience personality profile: A test to identify the affective dimensions of psychopathology in the context of precision psychotherapy

Published online by Cambridge University Press:  13 August 2021

R. Sperandeo ,
V. Cioffi ,
L. Mosca ,
B. Muzii  and
N. Maldonato
R. Sperandeo*
Affiliation:
Phenomena Research Group, Sipgi, Post graduate School of Psychotherapy, Torre Annunziata, Naples, Italy
V. Cioffi
Affiliation:
Phenomena Research Group, Sipgi, Post graduate School of Psychotherapy, Torre Annunziata, Naples, Italy
L. Mosca
Affiliation:
Phenomena Research Group, Sipgi, Post graduate School of Psychotherapy, Torre Annunziata, Naples, Italy
B. Muzii
Affiliation:
Department Of Humanistic Studies, University of Naples Federico II, Naples, Italy, Naples, Italy
N. Maldonato
Affiliation:
Neurosciences And Reproductive And Odontostomatological Sciences, University of Naples Federico II, Naples, Italy, Naples, Italy
*
*Corresponding author.

Abstract

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Introduction

Artificial 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 algorithms

Objectives

In 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 psychotherapy

Methods

Previously 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.

Results

The 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.

Conclusions

In 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.

Keywords

PersonalitypsychopathologytestArtificial Intelligence
Type
Abstract
Information
European Psychiatry , Volume 64 , Special Issue S1: Abstracts of the 29th European Congress of Psychiatry , April 2021 , pp. S455 - S456
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the European Psychiatric Association
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