Longitudinal planning for personalized health management using daily behavioral data
Longitudinal planning for personalized health management using daily behavioral data
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DOI：
10.1080/24725579.2019.1640814
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发表时间：
2019-07
影响因子：
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通讯作者：
Cao Xiao;Shupeng Gui;Ji Liu;Yu Cheng;Xiaoning Qian;Shuai Huang
中科院分区：
文献类型：
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作者：
Cao Xiao;Shupeng Gui;Ji Liu;Yu Cheng;Xiaoning Qian;Shuai Huang
缓解全球新兴的健康问题，如肥胖症，需要可扩展的解决方案，可以促进健康管理和促进健康的生活方式以外的临床环境。这种可扩展的解决方案虽然针对普通人群，但需要提供不仅适合用户自己的潜在生理动态而且适合他们的偏好和需求的个性化行为改变计划。用于监测人类行为（诸如身体活动和食物摄入）和健康状态（诸如BMI）的移动的健康设备和应用已经快速增长。然而，将这些嘈杂和动态的行为数据转化为个性化的纵向规划存在挑战。为了应对这些挑战，我们开发了一个集成框架，该框架将动态建模，稀疏学习，字典学习和矩阵完成相结合，将用户的行为数据转换为个性化的动态系统模型，并将其用作约束条件，以获得深度个性化的纵向健康计划。我们在真实世界的用户行为数据集上评估了所提出的框架，并证明了其有前途的实用性和有效性。
Abstract Mitigating globally emerging health problems such as obesity needs scalable solutions that can facilitate health management and promote healthier lifestyles outside of clinical settings. Such scalable solutions, while targeting general population, need to provide personalized behavior change plans that not only fit users’ own underlying physiologic dynamics but also suit their preferences and needs. There has been fast-growing development of mobile health devices and applications for monitoring of human behavior (such as physical activity and food intake) and health status such as BMI. However, there are challenges to translate these noisy and dynamic behavioral data into personalized longitudinal planning. To address such challenges, we develop an integrated framework that unifies dynamic modeling, sparse learning, dictionary learning and matrix completion to translate users’ behavioral data into personalized dynamic system models and use them as constraints for deriving deeply personalized longitudinal health plans. We evaluate the proposed framework on a real-world user behavioral dataset and demonstrate its promising utility and efficacy.