Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency

Villanueva Benito, Guillermo; Goldberg, Ximena; Brachowicz, Nicolai; Castaño Vinyals, Gemma; Blay, Natalia; Espinosa, Ana; Davidhi, Flavia; Torres, Diego; Kogevinas, Manolis; Cid Ibeas, Rafael de; Petrone, Paula M.

doi:http://dx.doi.org/10.1016/j.artmed.2024.102991

Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency

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Benito GV, Goldberg X, Brachowicz N, Castaño-Vinyals G, Blay N, Espinosa A, et al. Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency. Artif Intell Med. 2024 Nov;157:102991. DOI: 10.1016/j.artmed.2024.102991

Abstract

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.