Cox Christopher R, Moscardini Emma H, Cohen Alex S, Tucker Raymond P
Louisiana State University, Department of Psychology, USA.
Louisiana State University, Department of Psychology, USA.
Clin Psychol Rev. 2020 Dec;82:101940. doi: 10.1016/j.cpr.2020.101940. Epub 2020 Oct 23.
Machine learning is being used to discover models to predict the progression from suicidal ideation to action in clinical populations. While quantifiable improvements in prediction accuracy have been achieved over theory-driven efforts, models discovered through machine learning continue to fall short of clinical relevance. Thus, the value of machine learning for reaching this objective is hotly contested. We agree that machine learning, treated as a "black box" approach antithetical to theory-building, will not discover clinically relevant models of suicide. However, such models may be developed through deliberate synthesis of data- and theory-driven approaches. By providing an accessible overview of essential concepts and common methods, we highlight how generalizable models and scientific insight may be obtained by incorporating prior knowledge and expectations to machine learning research, drawing examples from suicidology. We then discuss challenges investigators will face when using machine learning to discover models of low prevalence outcomes, such as suicide.
机器学习正被用于发现模型，以预测临床人群中从自杀意念到自杀行为的进展。虽然与理论驱动的方法相比，预测准确性已有了可量化的提高，但通过机器学习发现的模型仍缺乏临床相关性。因此，机器学习对于实现这一目标的价值备受争议。我们认同，将机器学习视为与理论构建背道而驰的“黑箱”方法，是无法发现具有临床相关性的自杀模型的。然而，此类模型或许可以通过精心整合数据驱动和理论驱动的方法来开发。通过提供对基本概念和常用方法的易懂概述，我们强调了如何通过将先验知识和预期纳入机器学习研究来获得可推广的模型和科学见解，并从自杀学中举例说明。然后，我们讨论了研究人员在使用机器学习发现诸如自杀等低发生率结果的模型时将面临的挑战。
