远程血压管理的探索:基于移动数据的日常日常模式在健康管理中的应用
Yidan Xiang1, Shaochun Li1, Pengzhu Zhang1
1Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China.
Fundamental research
|June 27, 2024
概括
使用手机数据预测血压 (BP) 为高血压控制提供了一种新的方法. 这项研究表明,从移动数据中获得的日常日常模式可以有效预测血压,帮助持续监测健康状况.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 持续监测血压对于高血压管理至关重要.
- 传统的血压测量方法往往是侵入性的和不频繁的.
- 移动电话数据为不引人注目的健康监测提供了一个潜在的来源.
研究的目的:
- 开发和评估一种使用非生理手机数据预测血压的方法.
- 探索移动数据在远程监控中的应用,以进行连续,不引人注目的血压测量.
- 分析日常生活和血压变化之间的关系.
主要方法:
- 利用了来自320个人的半年数据集,包括电信和BP的测量.
- 采用分层贝叶斯主题模型从移动轨迹中提取日常日常模式.
- 使用主题概率分布和背景数据与长短期记忆 (LSTM) 和随机森林回归模型预测BP.
主要成果:
- 该研究成功地从移动数据中提取了基于位置的日常生活模式 (主题).
- 血压预测模型表现出有效性,LSTM在高依赖性数据方面表现优于其他模型.
- 当数据依赖性较低时,随机森林回归证明优越,验证了提取主题的实用性.
结论:
- 移动电话数据可以有效地用于预测血压,为高血压远程监测提供了一种新的方法.
- 提取的日常生活模式是血压的重要预测因素.
- 这项研究强调了使用无处不在的移动技术进行持续,不引人注目的健康监测的潜力.
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