Suicide attempt risk predicts inconsistent self-reported suicide attempts: A machine learning approach using longitudinal data

作者：E. F. Haghish, Nikolai Olavi Czajkowski, Fredrik Andreas Walby, Ping Qin, Bruno Laeng · 发表于：Journal of Affective Disorders · 年份：2024 · DOI：10.1016/j.jad.2024.03.133 · 被引用次数：19 · 研究领域：Suicide and Self-Harm Studies、COVID-19 and Mental Health、Mental Health via Writing

INTRODUCTION: Inconsistent self-reports of lifetime suicide attempts (LSAs) are a major obstacle for accurate assessment of suicidal behavior. This study is the first to posit that adolescents at higher risk report LSAs more consistently than those at lower risk, revealing a link between suicide attempt risk and consistent reporting. METHODS: A machine learning model was trained with 70 % of the baseline assessment data of a longitudinal sample of Norwegian adolescents (n = 10,739). The model was used to estimate the LSA risk score for the remaining 30 % of the testing dataset. The relationship between these baseline risk scores and the consistency of reporting LSAs was assessed using a 2-year follow-up reassessment of the testing dataset. RESULTS: Internalizing problems, optimism about the future, conduct problems, substance use, and disordered eating were important factors associated with suicide attempt risk. Of the participants, 63.41 % had inconsistent self-reports at the two-year follow-up. Adolescents who consistently reported LSAs had significantly higher scores of suicide attempt risk at baseline. Two logistic regression analyses confirmed an association between suicide attempt risk and inconsistent self-reported LSAs and showed that sex (being male), and lower levels of depression and conduct problems significantly predicted such inconsistencies. Those who inconsistently reported LSAs were more likely than the others to be classified by the model as false negatives ...