Phenomenological experience personality profile: A test to identify the affective dimensions of psychopathology in the context of precision psychotherapy. (13th April 2021)
- Record Type:
- Journal Article
- Title:
- Phenomenological experience personality profile: A test to identify the affective dimensions of psychopathology in the context of precision psychotherapy. (13th April 2021)
- Main Title:
- Phenomenological experience personality profile: A test to identify the affective dimensions of psychopathology in the context of precision psychotherapy
- Authors:
- Sperandeo, R.
Cioffi, V.
Mosca, L.
Muzii, B.
Maldonato, N. - Abstract:
- Abstract : 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 adaptAbstract : 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. … (more)
- Is Part Of:
- European psychiatry. Volume 64:Supplement 1(2021)
- Journal:
- European psychiatry
- Issue:
- Volume 64:Supplement 1(2021)
- Issue Display:
- Volume 64, Issue 1 (2021)
- Year:
- 2021
- Volume:
- 64
- Issue:
- 1
- Issue Sort Value:
- 2021-0064-0001-0000
- Page Start:
- S455
- Page End:
- S456
- Publication Date:
- 2021-04-13
- Subjects:
- Personality -- psychopathology -- test -- Artificial Intelligence
Psychiatry -- Periodicals
Mental illness -- Periodicals
Electronic journals
616.89 - Journal URLs:
- https://www.cambridge.org/core/journals/european-psychiatry ↗
http://www.clinicalkey.com/dura/browse/journalIssue/09249338 ↗
http://www.sciencedirect.com/science/journal/09249338 ↗
http://www.elsevier.com/homepage/elecserv.htt ↗ - DOI:
- 10.1192/j.eurpsy.2021.1217 ↗
- Languages:
- English
- ISSNs:
- 0924-9338
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3829.842700
British Library HMNTS - ELD Digital store - Ingest File:
- 18695.xml