Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approaches
DC Field | Value | Language |
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dc.contributor.author | Kim, Sunhae | - |
dc.contributor.author | Lee, Hye-Kyung | - |
dc.contributor.author | Lee, Kounseok | - |
dc.date.accessioned | 2023-08-16T07:53:20Z | - |
dc.date.available | 2023-08-16T07:53:20Z | - |
dc.date.created | 2023-07-21 | - |
dc.date.issued | 2021-04 | - |
dc.identifier.issn | 1661-7827 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/189176 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approaches | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Kounseok | - |
dc.identifier.doi | 10.3390/ijerph18073339 | - |
dc.identifier.scopusid | 2-s2.0-85102935608 | - |
dc.identifier.wosid | 000638505200001 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, v.18, no.7 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH | - |
dc.citation.title | INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH | - |
dc.citation.volume | 18 | - |
dc.citation.number | 7 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & EcologyPublic, Environmental & Occupational Health | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Public, Environmental & Occupational Health | - |
dc.subject.keywordPlus | PATIENT HEALTH QUESTIONNAIRE | - |
dc.subject.keywordPlus | DEPRESSION SEVERITY | - |
dc.subject.keywordPlus | MENTAL-HEALTHRISK-FACTORS | - |
dc.subject.keywordPlus | ITEM 9 | - |
dc.subject.keywordPlus | CARE | - |
dc.subject.keywordPlus | IDEATION | - |
dc.subject.keywordPlus | THOUGHTS | - |
dc.subject.keywordPlus | VALIDATION | - |
dc.subject.keywordPlus | VETERANS | - |
dc.subject.keywordAuthor | PHQ-9 | - |
dc.subject.keywordAuthor | suicide | - |
dc.subject.keywordAuthor | screening | - |
dc.subject.keywordAuthor | machine learning | - |
dc.identifier.url | https://www.mdpi.com/1660-4601/18/7/3339 | - |
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