Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques.
Our aim was to explore the possibility of using machine learning (ML) in predicting the onset and trajectories of depressive symptom in home-based older adults over a 7-year period. Depressive symptom data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2650) recru...
| Publicado en: | Aging & Mental Health Vol. 27; no. 1; pp. 8 - 18 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Jan2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=161031147&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 161031147 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 13607863 57E jtl: Aging & Mental Health issn: 13607863 maglogo: N pubinfo: dt: Jan2023 vid: 27 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 161031147 10.1080/13607863.2022.2031868 ppf: 8 ppct: 10 formats: tig: atl: Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques. aug: au: Lin, Shaowu Wu, Yafei He, Lingxiao Fang, Ya affil: The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China su: China Geriatrics & psychology Memory Cognition Sleep Mental depression Health Information resources Sociodemographic factors Old age Structural equation modeling Decision trees Support vector machines Research methodology Machine learning Random forest algorithms Risk assessment Receiver operating characteristic curves sug: subj: Geriatrics & psychology Memory Cognition Sleep Mental depression Health Information resources Sociodemographic factors Old age China Structural equation modeling Decision trees Support vector machines Research methodology Machine learning Random forest algorithms Risk assessment Receiver operating characteristic curves keyword: depressive symptoms home-based elderly long-term trajectory prediction depressive symptoms home-based elderly long-term trajectory prediction ab: Our aim was to explore the possibility of using machine learning (ML) in predicting the onset and trajectories of depressive symptom in home-based older adults over a 7-year period. Depressive symptom data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2650) recruited in the China Health and Retirement Longitudinal Study (CHARLS) were included in the current analysis. The latent class growth modeling (LCGM) and growth mixture modeling (GMM) were used to classify different trajectory classes. Based on the identified trajectory patterns, three ML classification algorithms (i.e. gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUC). Four trajectories were identified for the depressive symptoms: no symptoms (63.9%), depressive symptoms onset {incident increasing symptoms [new-onset increasing (16.8%)], chronic symptoms [slowly decreasing (12.5%), persistent high (6.8%)]}. Among the analyzed baseline variables, the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) score, cognition, sleep time, self-reported memory were the top five important predictors across all trajectories. The mean AUCs of the three predictive models had a range from 0.661 to 0.892. ML techniques can be robust in predicting depressive symptom onset and trajectories over a 7-year period with easily accessible sociodemographic and health information. Supplemental data for this article is available online at http://dx.doi.org/10.1080/13607863.2022.2031868 pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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