Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods
Sunhae Kim, Kounseok Lee Department of Psychiatry, Hanyang University Medical Center, Seoul, KoreaCorrespondence: Kounseok Lee Tel +82 2 2290 8423Fax +82 2 2298 2055Email dual@hanyang.ac.krPurpose: Depression is a symptom commonly encountered in primary care; however, it is often not detected by doc...
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| Formato: | article |
| Lenguaje: | EN |
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Dove Medical Press
2021
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| Acceso en línea: | https://doaj.org/article/32620ae534c2428a96db7434a1104356 |
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| Sumario: | Sunhae Kim, Kounseok Lee Department of Psychiatry, Hanyang University Medical Center, Seoul, KoreaCorrespondence: Kounseok Lee Tel +82 2 2290 8423Fax +82 2 2298 2055Email dual@hanyang.ac.krPurpose: Depression is a symptom commonly encountered in primary care; however, it is often not detected by doctors. Recently, disease diagnosis and treatment approaches have been attempted using smart devices. In this study, instrumental effectiveness was confirmed with the diagnostic meta-analysis of studies that demonstrated the diagnostic effectiveness of PHQ-9 for depression using mobile devices.Patients and Methods: We found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021. We performed a meta-analysis by including 1099 subjects in four studies. We performed a diagnostic meta-analysis according to the PHQ-9 cut-off score and machine learning algorithm techniques. Quality assessment was conducted using the QUADAS-2 tool. Data on the sensitivity and specificity of the studies included in the meta-analysis were extracted in a standardized format. Bivariate and summary receiver operating characteristic (SROC) curve were constructed using the metandi, midas, metabias, and metareg functions of the Stata algorithm meta-analysis words.Results: Using four studies out of the 5476 papers searched, a diagnostic meta-analysis of the PHQ-9 scores of 1099 people diagnosed with depression was performed. The pooled sensitivity and specificity were 0.797 (95% CI = 0.642– 0.895) and 0.85 (95% CI = 0.780– 0.900), respectively. The diagnostic odds ratio was 22.16 (95% CI = 7.273– 67.499). Overall, a good balance was maintained, and no heterogeneity or publication bias was presented.Conclusion: Through various machine learning algorithm techniques, it was possible to confirm that PHQ-9 depression screening in mobiles is an effective diagnostic tool when integrated into a diagnostic meta-analysis.Keywords: diagnostic meta-analysis, depression, Patient Health Questionnaire-9, machine learning, mobile, diagnosis |
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