Cited by

• 2019Prevalence, Onset, and Course of Suicidal Behavior Among Adolescents and Young Adults in Germany129 cited
• 2022An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach124 cited
• 2022Artificial intelligence and suicide prevention: A systematic review114 cited
• 2019Prediction models for high risk of suicide in Korean adolescents using machine learning techniques96 cited
• 2022Translating promise into practice: a review of machine learning in suicide research and prevention96 cited
• 2020Social media use and deliberate self-harm among youth: A systematized narrative review86 cited
• 2022The performance of machine learning models in predicting suicidal ideation, attempts, and deaths: A meta-analysis and systematic review85 cited
• 2021A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis77 cited
• 2022Suicidal behaviour prediction models using machine learning techniques: A systematic review74 cited
• 2022Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies73 cited
• 2020Machine learning enhances prediction of illness course: a longitudinal study in eating disorders73 cited
• 2020Identification of suicidality in adolescent major depressive disorder patients using sMRI: A machine learning approach.69 cited
• 2021Machine learning for suicidal ideation identification: A systematic literature review67 cited
• 2019Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning67 cited
• 2021Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features65 cited
• 2022Consumer Perspectives on the Use of Artificial Intelligence Technology and Automation in Crisis Support Services: Mixed Methods Study16 cited