Velupillai Sumithra, Hadlaczky Gergö, Baca-Garcia Enrique, Gorrell Genevieve M, Werbeloff Nomi, Nguyen Dong, Patel Rashmi, Leightley Daniel, Downs Johnny, Hotopf Matthew, Dutta Rina
Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Front Psychiatry. 2019 Feb 13;10:36. doi: 10.3389/fpsyt.2019.00036. eCollection 2019.
Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.
对全球心理健康服务而言，自杀行为风险评估是一项耗时且极不准确的工作。在过去50年里，大量工具被设计用于自杀风险评估，并在各种各样的人群中进行了测试，但研究表明，这些工具的阳性预测值较低。最近，机器学习和自然语言处理等研究领域在大型数据集上的进展已在医疗保健方面显示出有前景的结果，并且可能在推进精准医学方面带来重要转变。在这篇概念性综述中，我们讨论了已有的风险评估工具以及用于识别自杀行为和风险的新型数据驱动方法的实例。我们对这些应用于心理健康相关数据的优缺点提供了一个观点，并提出了能够改进临床实践的研究方向。
