心理健康
逻辑回归
医学
自杀未遂
毒物控制
队列
预测建模
自杀预防
伤害预防
职业安全与健康
流行病学
危害
心理学
精神科
人口学
老年学
医疗急救
机器学习
计算机科学
社会心理学
内科学
病理
社会学
作者
Catherine McHugh,Nicholas Ho,Frank Iorfino,Jacob J. Crouse,Alissa Nichles,Natalia Zmicerevska,Elizabeth Scott,Nick Glozier,Ian B. Hickie
标识
DOI:10.1007/s00127-022-02415-7
摘要
Machine learning (ML) has shown promise in modelling future self-harm but is yet to be applied to key questions facing clinical services. In a cohort of young people accessing primary mental health care, this study aimed to establish (1) the performance of models predicting deliberate self-harm (DSH) compared to suicide attempt (SA), (2) the performance of models predicting new-onset or repeat behaviour, and (3) the relative importance of factors predicting these outcomes.802 young people aged 12-25 years attending primary mental health services had detailed social and clinical assessments at baseline and 509 completed 12-month follow-up. Four ML algorithms, as well as logistic regression, were applied to build four distinct models.The mean performance of models predicting SA (AUC: 0.82) performed better than the models predicting DSH (AUC: 0.72), with mean positive predictive values (PPV) approximately twice that of the prevalence (SA prevalence 14%, PPV: 0.32, DSH prevalence 22%, PPV: 0.40). All ML models outperformed standard logistic regression. The most frequently selected variable in both models was a history of DSH via cutting.History of DSH and clinical symptoms of common mental disorders, rather than social and demographic factors, were the most important variables in modelling future behaviour. The performance of models predicting outcomes in key sub-cohorts, those with new-onset or repetition of DSH or SA during follow-up, was poor. These findings may indicate that the performance of models of future DSH or SA may depend on knowledge of the individual's recent history of either behaviour.
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