Guo Qian, Chen Peiyuan
School of Economics and Management, Anhui Normal University, Wuhu, China.
Oregon State University, Corvallis, OR, United States.
Front Public Health. 2024 Dec 18;12:1486930. doi: 10.3389/fpubh.2024.1486930. eCollection 2024.
With the intensification of global aging, health management for the older adult has become a significant societal concern. Addressing challenges such as data diversity, health status complexity, long-term dependence, and data privacy is crucial for predicting older adult health behaviors.
This study designs and implements a smart older adult care service model incorporating modules like multimodal data fusion, data loss processing, nonlinear prediction, emergency detection, and privacy protection. It leverages multi-source datasets and market research for accurate health behavior prediction and dynamic management.
The model demonstrates excellent performance in health behavior prediction, emergency detection, and delivering personalized services. Experimental results show an increase in accuracy and robustness in health behavior prediction.
The model effectively addresses the needs of smart older adult care, offering a promising solution to enhance prediction accuracy and system robustness. Future improvements, integrating more data and optimizing technology, will strengthen its potential for providing comprehensive support in older adult care services.
随着全球老龄化加剧，老年人的健康管理已成为一项重大的社会关切。应对数据多样性、健康状况复杂性、长期依赖性和数据隐私等挑战对于预测老年人的健康行为至关重要。
本研究设计并实施了一种智能老年护理服务模型，该模型包含多模态数据融合、数据丢失处理、非线性预测、应急检测和隐私保护等模块。它利用多源数据集和市场研究进行准确的健康行为预测和动态管理。
该模型在健康行为预测、应急检测和提供个性化服务方面表现出色。实验结果表明，健康行为预测的准确性和稳健性有所提高。
该模型有效满足了智能老年护理的需求，为提高预测准确性和系统稳健性提供了一个有前景的解决方案。未来通过整合更多数据和优化技术进行改进，将增强其在老年护理服务中提供全面支持的潜力。
