| Zugriffsnummer | 52646 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine |
| Autor(in); Institution |
Lopez Steinmetz, Lorena Cecilia; Inverse Modeling and Machine Learning, Chair of Uncertainty, Institute of Software Engineering and Theoretical Computer Science, Faculty IV Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, GERMANY; Instituto de Investigaciones Psicológicas (IIPsi), Facultad de Psicología, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Córdoba (UNC), Córdoba, ARGENTINA
Sison, Margarita; Berlin Center for Advanced Neuroimaging (BCAN), Charité – Universitätsmedizin Berlin, Berlin, GERMANY
Zhumagambetov, Rustam; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Godoy, Juan Carlos; Instituto de Investigaciones Psicológicas (IIPsi), Facultad de Psicología, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Córdoba (UNC), Córdoba, ARGENTINA
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| Quelle/Jahr | Frontiers in Psychiatry: 15 (2024), 01 - 15 |
| Availability | [online only] |
| ISSN | 1664-0640 (online) |
| DOI | |
| URL | |
| Verlag | Lausanne: Frontiers |
| Freie Schlagworte | depression prediction ; COVID-19 pandemic ; machine learning ; classification ; regression ; college students ; longitudinal survey ; Argentina |
| Zusammenfassung | Introduction: The COVID-19 pandemic has exacerbated mental health challenges, particularly depression among college students. Detecting at-risk students early is crucial but remains challenging, particularly in developing countries. Utilizing data-driven predictive models presents a viable solution to address this pressing need. Aims: 1) To develop and compare machine learning (ML) models for predicting depression in Argentinean students during the pandemic. 2) To assess the performance of classification and regression models using appropriate metrics. 3) To identify key features driving depression prediction. |
| Kostenfreier Zugang | Open Access Gold |
| Rechteinformation | CC BY 4.0 ; Creative Commons Attribution 4.0 License |
| Themenbereich der Metrologie | Metrologie in der Medizin |
Zitierung
Lopez Steinmetz, L. C., Sison, M., Zhumagambetov, R., Godoy, J. C., & Haufe, S. (2024). Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine. Frontiers in Psychiatry, 15, 01–15. https://doi.org/10.3389/fpsyt.2024.1376784