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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Kim S, Lee K
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	diagnostic meta-analysis depression patient health questionnaire-9 machine learning mobile diagnosis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/32620ae534c2428a96db7434a1104356
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:32620ae534c2428a96db7434a1104356
record_format	dspace
spelling	oai:doaj.org-article:32620ae534c2428a96db7434a11043562021-11-21T19:08:51ZScreening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods1178-2021https://doaj.org/article/32620ae534c2428a96db7434a11043562021-11-01T00:00:00Zhttps://www.dovepress.com/screening-for-depression-in-mobile-devices-using-patient-health-questi-peer-reviewed-fulltext-article-NDThttps://doaj.org/toc/1178-2021Sunhae 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diagnostic effectiveness of PHQ-9 for depression using mobile devices.Patients and Methods: We found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021.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, diagnosisKim SLee KDove Medical Pressarticlediagnostic meta-analysisdepressionpatient health questionnaire-9machine learningmobilediagnosisNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 17, Pp 3415-3430 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	diagnostic meta-analysis depression patient health questionnaire-9 machine learning mobile diagnosis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	diagnostic meta-analysis depression patient health questionnaire-9 machine learning mobile diagnosis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Kim S Lee K Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods
description	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
format	article
author	Kim S Lee K
author_facet	Kim S Lee K
author_sort	Kim S
title	Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods
title_short	Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods
title_full	Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods
title_fullStr	Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods
title_full_unstemmed	Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods
title_sort	screening for depression in mobile devices using patient health questionnaire-9 (phq-9) data: a diagnostic meta-analysis via machine learning methods
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/32620ae534c2428a96db7434a1104356
work_keys_str_mv	AT kims screeningfordepressioninmobiledevicesusingpatienthealthquestionnaire9phq9dataadiagnosticmetaanalysisviamachinelearningmethods AT leek screeningfordepressioninmobiledevicesusingpatienthealthquestionnaire9phq9dataadiagnosticmetaanalysisviamachinelearningmethods
_version_	1718418693387452416

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

Ejemplares similares