Brain and Mind Centre, University of Sydney.
School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia.
Curr Opin Psychiatry. 2020 Jul;33(4):369-374. doi: 10.1097/YCO.0000000000000609.
In recent years there has been interest in the use of machine learning in suicide research in reaction to the failure of traditional statistical methods to produce clinically useful models of future suicide. The current review summarizes recent prediction studies in the suicide literature including those using machine learning approaches to understand what value these novel approaches add.
Studies using machine learning to predict suicide deaths report area under the curve that are only modestly greater than, and sensitivities that are equal to, those reported in studies using more conventional predictive methods. Positive predictive value remains around 1% among the cohort studies with a base rate that was not inflated by case-control methodology.
Machine learning or artificial intelligence may afford opportunities in mental health research and in the clinical care of suicidal patients. However, application of such techniques should be carefully considered to avoid repeating the mistakes of existing methodologies. Prediction studies using machine-learning methods have yet to make a major contribution to our understanding of the field and are unproven as clinically useful tools.
近年来，由于传统统计学方法未能建立具有临床应用价值的未来自杀预测模型，人们对机器学习在自杀研究中的应用产生了兴趣。本综述总结了自杀研究领域的最新预测研究，包括使用机器学习方法来理解这些新方法的价值。
使用机器学习预测自杀死亡的研究报告的曲线下面积仅略高于，敏感性与使用更传统预测方法的研究报告的敏感性相当。在没有使用病例对照方法学来夸大基线率的队列研究中，阳性预测值仍保持在 1%左右。
机器学习或人工智能可能为精神健康研究和自杀患者的临床护理提供机会。然而，应该谨慎考虑应用这些技术，以避免重复现有方法学的错误。使用机器学习方法的预测研究尚未对我们对该领域的理解做出重大贡献，并且尚未证明其作为临床有用工具的实用性。
