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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sunhae Kim & Hye-Kyung Lee & Kounseok Lee, 2021. "Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approaches," IJERPH, MDPI, vol. 18(7), pages 1-10, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:7:p:3339-:d:523042
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    References listed on IDEAS

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    1. Mackelprang, J.L. & Bombardier, C.H. & Fann, J.R. & Temkin, N.R. & Barber, J.K. & Dikmen, S.S., 2014. "Rates and predictors of suicidal ideation during the first year after traumatic brain injury," American Journal of Public Health, American Public Health Association, vol. 104(7), pages 100-107.
    2. Xieyining Huang & Jessica D Ribeiro & Katherine M Musacchio & Joseph C Franklin, 2017. "Demographics as predictors of suicidal thoughts and behaviors: A meta-analysis," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-21, July.
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    2. Tauqeer Hussain Mallhi & Naveed Ahmad & Muhammad Salman & Nida Tanveer & Shahid Shah & Muhammad Hammad Butt & Ahmed D. Alatawi & Nasser Hadal Alotaibi & Hidayat Ur Rahman & Abdulaziz Ibrahim Alzarea &, 2022. "Estimation of Psychological Impairment and Coping Strategies during COVID-19 Pandemic among University Students in Saudi Arabia: A Large Regional Analysis," IJERPH, MDPI, vol. 19(21), pages 1-16, November.

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