Key result
Deep learning exercise model dynamically predicts heart health status in the elderly.
A deep learning-based model is proposed to evaluate and predict heart health status in the elderly using exercise health data.
May support AI tools for elderly cardiac monitoring; leaves open prospective validation before clinical adoption.
With the accelerating rate of population aging in China, the health of the elderly has received more and more attention and has become one of the most important issues in the elderly care industry. Because of insufficient research on the personal health of the elderly, the value of medical examination data cannot be fully exploited, many physical indicators have a certain impact on overall health or heart health, and there are few studies on heart health assessment. This paper proposes a deep learning-based elderly management analysis method of human exercise health level, using the exercise health management model to evaluate the heart health level of the elderly. Firstly, the indicators to measure heart health are proposed through traditional expert knowledge and personal health index to analyze heart health. Through dynamic assessment, predict the heart health status at the next time point, analyze possible heart diseases, and provide corresponding methods for the health of the elderly, which helps improve the physical health of the elderly. Quality of life provides assistance to meet the needs of improving the health of older adults.
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Xiao et al. (2022) studied Heart health in the elderly. Deep learning-based exercise health management model was evaluated on Heart health status prediction. A deep learning-based exercise health management model was proposed to dynamically assess and predict heart health status in the elderly.
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