Geriatric depression and anxiety screening via deep learning using activity tracking and sleep data.
Int J Geriatr Psychiatry
; 39(2): e6071, 2024 Feb.
Article
en En
| MEDLINE
| ID: mdl-38372966
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
Límite:
Aged
/
Humans
Idioma:
En
Revista:
Int J Geriatr Psychiatry
Asunto de la revista:
GERIATRIA
/
PSIQUIATRIA
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Reino Unido