A Machine Learning Approach to Relationships Among Alexithymia Components.

Briganti, Giovanni; Scutari, Marco; Linkowski, Paul

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A Machine Learning Approach to Relationships Among Alexithymia Components.

Briganti, Giovanni; Scutari, Marco; Linkowski, Paul

2020 • In Psychiatria Danubina, 32 (Suppl 1), p. 180-187

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https://hdl.handle.net/20.500.12907/43980

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32890387

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Keywords :

Affective Symptoms; Emotions; Fantasy; Humans; Machine Learning; Surveys and Questionnaires

Abstract :

[en] BACKGROUND: The aim of this paper is to explore the network structures of alexithymia components and compare results with relevant prior literature. SUBJECTS AND METHODS: In a large sample of university students, undirected and directed network structures of items from the Bermond Vorst Alexithymia Questionnaire form B are estimated with state-of-the-art network analysis and structure learning tools. Centrality estimates are used to address the topic of item redundancy and select relevant alexithymia components to study. RESULTS: Alexithymia components present positive as well as negative connections; poor fantasy and emotional insight are identified as central items in the network. CONCLUSIONS: The undirected network structure of alexithymia components reports new features with respect to prior literature, and the directed network structures offers new insight on the construct.

Disciplines :

Psychiatry

Author, co-author :

Briganti, Giovanni ; Université de Mons - UMONS > Faculté de Médecine et de Pharmacie > Service de Neurosciences ; Unit of Epidemiology, Biostatistics and Clinical Research, Université libre de

Scutari, Marco

Linkowski, Paul

Language :

English

Title :

A Machine Learning Approach to Relationships Among Alexithymia Components.

Publication date :

September 2020

Journal title :

Psychiatria Danubina

ISSN :

0353-5053

Publisher :

Faculty of Forestry, University of Zagreb, Hr

Volume :

Issue :

Suppl 1

Pages :

180-187

Peer reviewed :

Peer Reviewed verified by ORBi

Research institute :

Biosciences

Available on ORBi UMONS :

since 16 December 2022

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