哪些 PHQ-9 条目可以有效地筛查自杀？机器学习方法。

Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approaches.

机构信息

Department of Psychiatry, Hanyang University Medical Center, Seoul 04763, Korea.

Department of Nursing, College of Nursing and Health, Kongju National University, Kognju 32588, Korea.

出版信息

Int J Environ Res Public Health. 2021 Mar 24;18(7):3339. doi: 10.3390/ijerph18073339.

DOI:10.3390/ijerph18073339

PMID:33804879

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8036742/

Abstract

(1) Background: The Patient Health Questionnaire-9 (PHQ-9) is a tool that screens patients for depression in primary care settings. In this study, we evaluated the efficacy of PHQ-9 in evaluating suicidal ideation (2) Methods: A total of 8760 completed questionnaires collected from college students were analyzed. The PHQ-9 was scored in combination with and evaluated against four categories (PHQ-2, PHQ-8, PHQ-9, and PHQ-10). Suicidal ideations were evaluated using the Mini-International Neuropsychiatric Interview suicidality module. Analyses used suicide ideation as the dependent variable, and machine learning (ML) algorithms, k-nearest neighbors, linear discriminant analysis (LDA), and random forest. (3) Results: Random forest application using the nine items of the PHQ-9 revealed an excellent area under the curve with a value of 0.841, with 94.3% accuracy. The positive and negative predictive values were 84.95% (95% CI = 76.03-91.52) and 95.54% (95% CI = 94.42-96.48), respectively. (4) Conclusion: This study confirmed that ML algorithms using PHQ-9 in the primary care field are reliably accurate in screening individuals with suicidal ideation.

摘要

（1）背景：PHQ-9 是一种在初级保健环境中筛查患者抑郁的工具。在这项研究中，我们评估了 PHQ-9 在评估自杀意念方面的功效。（2）方法：分析了共 8760 份完成的大学生问卷。PHQ-9 与四个类别（PHQ-2、PHQ-8、PHQ-9 和 PHQ-10）相结合进行评分，并进行评估。使用 Mini-International Neuropsychiatric Interview 自杀模块评估自杀意念。分析使用自杀意念作为因变量，使用机器学习（ML）算法、k-最近邻、线性判别分析（LDA）和随机森林。（3）结果：使用 PHQ-9 的九个项目进行随机森林应用，曲线下面积达到了 0.841，准确率为 94.3%。阳性预测值和阴性预测值分别为 84.95%（95% CI = 76.03-91.52）和 95.54%（95% CI = 94.42-96.48）。（4）结论：本研究证实，在初级保健领域使用 PHQ-9 的 ML 算法在筛查有自杀意念的个体方面具有可靠的准确性。