Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approachesopen access
- Authors
- Kim, Sunhae; Lee, Hye-Kyung; Lee, Kounseok
- Issue Date
- Apr-2021
- Publisher
- MDPI
- Keywords
- PHQ-9; suicide; screening; machine learning
- Citation
- INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, v.18, no.7
- Indexed
- SCIE
SSCI
SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
- Volume
- 18
- Number
- 7
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/189176
- DOI
- 10.3390/ijerph18073339
- ISSN
- 1661-7827
- 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.
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