Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 김건하 | - |
dc.date.accessioned | 2024-05-20T16:31:11Z | - |
dc.date.available | 2024-05-20T16:31:11Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 0885-6230 | - |
dc.identifier.other | OAK-34893 | - |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/268445 | - |
dc.description.abstract | Background: Geriatric depression and anxiety have been identified as mood disorders commonly associated with the onset of dementia. Currently, the diagnosis of geriatric depression and anxiety relies on self-reported assessments for primary screening purposes, which is uncomfortable for older adults and can be prone to misreporting. When a more precise diagnosis is needed, additional methods such as in-depth interviews or functional magnetic resonance imaging are used. However, these methods can not only be time-consuming and costly but also require systematic and cost-effective approaches. Objective: The main objective of this study was to investigate the feasibility of training an end-to-end deep learning (DL) model by directly inputting time-series activity tracking and sleep data obtained from consumer-grade wrist-worn activity trackers to identify comorbid depression and anxiety. Methods: To enhance accuracy, the input of the DL model consisted of step counts and sleep stages as time series data, along with minimal depression and anxiety assessment scores as non-time-series data. The basic structure of the DL model was designed to process mixed-input data and perform multi-label-based classification for depression and anxiety. Various DL models, including the convolutional neural network (CNN) and long short-term memory (LSTM), were applied to process the time-series data, and model selection was conducted by comparing the performances of the hyperparameters. Results: This study achieved significant results in the multi-label classification of depression and anxiety, with a Hamming loss score of 0.0946 in the Residual Network (ResNet), by applying a mixed-input DL model based on activity tracking data. The comparison of hyper-parameter performance and the development of various DL models, such as CNN, LSTM, and ResNet contributed to the optimization of time series data processing and achievement of meaningful results. Conclusions: This study can be considered as the first to develop a mixed-input DL model based on activity tracking data for the multi-label identification of late-life depression and anxiety. The findings of the study demonstrate the feasibility and potential of using consumer-grade wrist-worn activity trackers in conjunction with DL models to improve the identification of comorbid mental health conditions in older adults. The study also established a multi-label classification framework for identifying the complex symptoms of depression and anxiety. © 2024 John Wiley & Sons Ltd. | - |
dc.language | English | - |
dc.publisher | John Wiley and Sons Ltd | - |
dc.subject | activity tracking | - |
dc.subject | anxiety | - |
dc.subject | deep learning | - |
dc.subject | depression | - |
dc.subject | mixed input model | - |
dc.subject | multi-label classification | - |
dc.subject | sleep | - |
dc.subject | step counts | - |
dc.subject | time-series data | - |
dc.title | Geriatric depression and anxiety screening via deep learning using activity tracking and sleep data | - |
dc.type | Article | - |
dc.relation.issue | 2 | - |
dc.relation.volume | 39 | - |
dc.relation.index | SCIE | - |
dc.relation.index | SSCI | - |
dc.relation.index | SCOPUS | - |
dc.relation.journaltitle | International Journal of Geriatric Psychiatry | - |
dc.identifier.doi | 10.1002/gps.6071 | - |
dc.identifier.wosid | WOS:001164478400001 | - |
dc.identifier.scopusid | 2-s2.0-85185614557 | - |
dc.author.google | Lee | - |
dc.author.google | Tae-Rim | - |
dc.author.google | Kim | - |
dc.author.google | Geon Ha | - |
dc.author.google | Choi | - |
dc.author.google | Mun-Taek | - |
dc.contributor.scopusid | 김건하(36554502600) | - |
dc.date.modifydate | 20240520120602 | - |