2026-04-17T16:17:37Zhttps://recercat.cat/oai/request
oai:recercat.cat:10230/688192025-12-12T02:26:37Zcom_2072_6col_2072_452952
RECERCAT author Villanueva Benito, Guillermo author Goldberg, Ximena author Brachowicz, Nicolai author Castaño Vinyals, Gemma author Blay, Natalia author Espinosa, Ana author Davidhi, Flavia author Torres, Diego author Kogevinas, Manolis author Cid Ibeas, Rafael de author Petrone, Paula M. 2024-11-26T07:34:50Z2024-11-26T07:34:50Z2024 Background & objectives: Mental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighted gaps in preparedness, emphasizing the need for early identification of at-risk groups and targeted interventions. This study aims to develop a risk assessment tool for anxiety, depression, and self-perceived stress using machine learning (ML) and explainable AI to identify key risk factors and stratify the population into meaningful risk profiles. Methods: We utilized a cohort of 9291 individuals from Northern Spain, with extensive post-COVID-19 mental health surveys. ML classification algorithms predicted depression, anxiety, and self-reported stress in three classes: healthy, mild, and severe outcomes. A novel combination of SHAP (SHapley Additive exPlanations) and UMAP (Uniform Manifold Approximation and Projection) was employed to interpret model predictions and facilitate the identification of high-risk phenotypic clusters. Results: The mean macro-averaged one-vs-one AUROC was 0.77 (± 0.01) for depression, 0.72 (± 0.01) for anxiety, and 0.73 (± 0.02) for self-perceived stress. Key risk factors included poor self-reported health, chronic mental health conditions, and poor social support. High-risk profiles, such as women with reduced sleep hours, were identified for self-perceived stress. Binary classification of healthy vs. at-risk classes yielded F1-Scores over 0.70. Conclusions: Combining SHAP with UMAP for risk profile stratification offers valuable insights for developing effective interventions and shaping public health policies. This data-driven approach to mental health preparedness, when validated in real-world scenarios, can significantly address the mental health impact of public health crises like COVID-19. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess COVID-19 Machine learning Mental health Preparedness Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion