Department of Psychiatry, Hanyang University Medical Center, Seoul 04763, Korea.
Department of Nursing, College of Nursing and Health, Kongju National University, Kognju 32588, Korea.
Int J Environ Res Public Health. 2021 Mar 24;18(7):3339. doi: 10.3390/ijerph18073339.
(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.
（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。
