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

Sunhae Kim, Hye-Kyung Lee and Kounseok Lee
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Sunhae Kim: Department of Psychiatry, Hanyang University Medical Center, Seoul 04763, Korea
Hye-Kyung Lee: Department of Nursing, College of Nursing and Health, Kongju National University, Kognju 32588, Korea
Kounseok Lee: Department of Psychiatry, Hanyang University Medical Center, Seoul 04763, Korea

IJERPH, 2021, vol. 18, issue 7, 1-10

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.

Keywords: PHQ-9; suicide; screening; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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