用因果推理和多臂强盗设计数字健康干预:一项审查
Radoslava Švihrová1,2, Alvise Dei Rossi2,3, Davide Marzorati2
1Institute of Computer Science, Faculty of Science, University of Bern, Bern, Switzerland.
本研究探讨了人工智能驱动的移动健康应用程序,以改变行为,专注于使用可穿戴数据进行个性化干预,以改善健康和生活质量.
科学领域:
- 数字健康数字健康
- 行为科学 行为科学
- 人工智能的人工智能
背景情况:
- 非传染性疾病导致全球74%的死亡,生活方式是关键因素.
- 移动健康 (mHealth) 和可穿戴设备为个性化的健康干预提供了新的途径.
- 生活方式医学和行为改变原则可以转化为有效的数字工具.
研究的目的:
- 为生活方式医学提供人工智能驱动的mHealth的进展,挑战和未来方向的概述.
- 讨论使用实时数据的即时适应性干预 (JITAI) 的设计.
- 呈现一个框架来定制行为变化战略的动态.
主要方法:
- 利用智能手机和可穿戴设备的数据进行持续监控.
- 利用贝叶斯式多臂强盗从强化学习到干预量身定制.
- 应用因果推理方法来结合用户行为假设和公正的评估.
主要成果:
- 展示了个性化,数据驱动的健康干预的通用化方法.
- 展示了适应性干预用于实时行为变化的使用.
- 提出了评估个人和人口层面干预有效性的策略.
结论:
- 人工智能驱动的移动健康,特别是JITAI,具有促进更健康的生活方式的巨大潜力.
- 基于真实世界的数据进行干预的动态个性化是可行的和有效的.
- 拟议的框架可适应各种行为变化用例,提高公共卫生结果.
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