Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approaches
Bibliographic Data
| ID | 15511726 |
|---|---|
| Authors | Sunhae Kim (0000-0001-7443-7684, Hanyang University Medical Center), Hye-Kyung Lee (0000-0002-8135-9378, Kongju National University), Kounseok Lee (0000-0002-6084-5043, Hanyang University Medical Center, corresponding author) |
| Year | 2021 |
| Volume | 18 |
| Issue | 7 |
| Pages | 3339-3339 |
| Publication date | 2021-03-24 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph18073339 |
| PMID | 33804879 |
| OpenAlex | W3145871323 |
| Language | EN |
| Citations received | 14 |
| References cited | 46 |
(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
Anxiety · Depression (economics · Depressive symptoms · Machine learning · Medical emergency · Patient Health Questionnaire · Poison control · Psychiatry · Random forest · Suicidal ideation · Suicide prevention · Clinical Psychology · Computer Science · Medicine · Mental Health Treatment and Access · Psychosomatic Disorders and Their Treatments · Suicide and Self-Harm Studies
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| Unique citing works | 14 |
|---|---|
| Citations per year | 3,5 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 14 |