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anxiety and depression profiling and risk assessment in the aftermath of an emergency Destination: mk:title Children: Item: Title: 1 Introduction Destination: mk:H1_1 Item: Title: 2 Methods Destination: mk:H1_2 Children: Item: Title: 2.1 Data collection Destination: mk:H2_1 Item: Title: 2.2 Data preprocessing Destination: mk:H2_2 Children: Item: Title: 2.2.1 Data selection Destination: mk:H3_3 Item: Title: 2.2.2 Categorization of mental health outcomes into severity classes Destination: mk:H3_4 Item: Title: 2.2.3 Imputation Destination: mk:H3_5 Item: Title: 2.2.4 Categorical variable encoding Destination: mk:H3_6 Item: Title: 2.2.5 Feature scaling Destination: mk:H3_7 Item: Title: 2.3 Machine learning-based predictive modeling Destination: mk:H2_8 Children: Item: Title: 2.3.1 Classification tasks Destination: mk:H3_9 Item: Title: 2.3.2 Machine learning classifiers Destination: mk:H3_10 Item: Title: 2.3.3 Evaluation of the models Destination: mk:H3_11 Item: Title: 2.3.4 Feature selection Destination: mk:H3_12 Item: Title: 2.3.5 Hyperparameter tuning Destination: mk:H3_13 Item: Title: 2.4 Model interpretation using SHAP Destination: mk:H2_14 Item: Title: 2.5 Stratification by personalized risk profile using SHAP and UMAP Destination: mk:H2_15 Item: Title: 3 Results Destination: mk:H1_3 Children: Item: Title: 3.1 Classification performance Destination: mk:H2_16 Item: Title: 3.2 Mental health risk drivers Destination: mk:H2_17 Item: Title: 3.3 Individual risk profiles analysis and stratification Destination: mk:H2_18 Item: Title: 4 Discussion Destination: mk:H1_4 Item: Title: 5 Conclusion Destination: mk:H1_5 Item: Title: Ethical approval Destination: mk:H1_6 Item: Title: CRediT authorship contribution statement Destination: mk:H1_7 Item: Title: Declaration of Generative AI and AI-assisted technologies in the writing process Destination: mk:H1_8 Item: Title: Declaration of competing interest Destination: mk:HH1_2 Item: Title: Data availability Destination: 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Artificial Intelligence In Medicine, 157 (2024) 102991. doi:10.1016/j.artmed.2024.102991 Machine learning Mental health COVID-19 Preparedness Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency Guillermo Villanueva Benito Ximena Goldberg Nicolai Brachowicz Gemma Castaño-Vinyals Natalia Blay Ana Espinosa Flavia Davidhi Diego Torres Manolis Kogevinas Rafael de Cid Paula Petrone VoR 9th November 2024 Acrobat Distiller 8.1.0 (Windows) Machine learning,Mental health,COVID-19,Preparedness 9th November 2024 sciencedirect.com elsevier.com true 2010-04-23 10.1016/j.artmed.2024.102991 noindex journal © 2024 The Authors. 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