Benedetta Pongiglione, Aleksandra Torbica, Michael K Gusmano
Inequalities in avoidable hospitalisation in large urban areas: retrospective observational study in the metropolitan area of Milan
BMJ open
understanding intricate influences risk factors contributing suicide among young individuals remains challenge current study employed interpretable machine learning network analysis unravel critical suicideassociated factors chinese university students total 68071 students recruited sep 2016 sep 2020 china students reported lifetime experiences suicidal thoughts behaviors categorized suicide ideation si suicide plan sp suicide attempt sa assessed 36 suicideassociated factors including psychopathology family environment life events stigma local interpretations provided using shapley additive explanation shap interaction values mixed graphical model facilitated global understanding interplay local explanations based shap interaction values suggested psychoticism depression severity emerged pivotal factors si paranoid ideation strongly correlated sp sa addition childhood neglect significantly predicted sa regarding mixed graphical model hierarchical structure emerged suggesting family factors preceded proximal psychopathological factors abuse neglect retaining unique effects centrality indices derived network highlighted importance subjective socioeconomic status education connecting various risk factors proximity psychopathological factors suicidality underscores significance global structures network suggested cooccurring factors influence suicidal behavior hierarchical manner therefore prospective prevention strategies take account hierarchical structure unique trajectories factors