Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency
Villanueva Benito, Guillermo (Universitat Pompeu Fabra)
Goldberg, Ximena (Institut de Salut Global de Barcelona)
Brachowicz, Nicolai (Institut de Salut Global de Barcelona)
Castaño-Vinyals, Gemma (Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública)
Blay, Natalia (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Espinosa, Ana (Institut de Salut Global de Barcelona)
Davidhi, Flavia (Institut de Salut Global de Barcelona)
Torres, Diego (Institut de Salut Global de Barcelona)
Kogevinas, Manolis (Institut de Salut Global de Barcelona)
de Cid, Rafael (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Petrone, Paula Marcela (Institut de Salut Global de Barcelona)

Fecha:	2024
Resumen:	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.
Ayudas:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-01563 Instituto de Salud Carlos III PI18/01512 "la Caixa" Foundation SR20-01024
Nota:	Altres ajuts: "Centro de Excelencia Severo Ochoa 2019-2023" Program (CEX2018-000806-S, CEX2023-0001290-S)
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Machine learning ; Mental health ; COVID-19 ; Preparedness
Publicado en:	Artificial Intelligence in Medicine, Vol. 157 (November 2024) , ISSN 1873-2860

DOI: 10.1016/j.artmed.2024.102991

14 p, 5.1 MB

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