@article{ref1,
title="Which PHQ-9 items can effectively screen for suicide? Machine learning approaches",
journal="International journal of environmental research and public health",
year="2021",
author="Kim, Sunhae and Lee, Hye-Kyung and Lee, Kounseok",
volume="18",
number="7",
pages="-",
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.<p /> <p>Language: en</p>",
language="en",
issn="1661-7827",
doi="10.3390/ijerph18073339",
url="http://dx.doi.org/10.3390/ijerph18073339"
}