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Using computational psychiatry for identifying risk factors for depressive disorder

Published online by Cambridge University Press:  26 August 2025

I. Marinić  and
L. Mužinić Marinić
I. Marinić*
Affiliation:
Department of Psychiatry, Dubrava University Hospital, Zagreb, Croatia
L. Mužinić Marinić
Affiliation:
Department of Psychiatry, Dubrava University Hospital, Zagreb, Croatia
*
*Corresponding author.

Abstract

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Introduction

Computational psychiatry uses computer models to improve the understanding, diagnosis, and treatment of mental health disorders. These models can integrate large datasets from various sources, including genetic, neurobiological, and environmental factors, to predict the likelihood of developing depression.

Objectives

The aim of the study is to explore the potential of computational psychiatry methods in identifying risk factors for the development of depressive disorders.

Methods

A review of relevant studies was conducted using the PubMed database. The search focused on articles examining computational psychiatry approaches, particularly those assessing risk factors associated with the onset of depressive disorders.

Results

The study highlights computational models that show potential in identifying risk factors for depressive disorders.

Conclusions

Computational psychiatry offers new insights into identifying risk factors for psychiatric disorders and has the potential to contribute to the prevention and treatment of depressive disorders. However, further research is needed to improve the generalizability and applicability of the models.

Disclosure of Interest

None Declared

Information

Type
Abstract
Information
European Psychiatry , Volume 68 , Special Issue S1: Abstracts of the 33rd European Congress of Psychiatry , April 2025 , pp. S659
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 (https://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), 2025. Published by Cambridge University Press on behalf of European Psychiatric Association
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