数据驱动的可解释政策构建,以实现个性化的移动健康服务
Dimitris Bertsimas1, Predrag Klasnja2, Susan Murphy3
1Sloan School of Management Massachusetts Institute of Technology Cambridge, USA.
概括
本研究介绍了最佳政策树+ (OPT+),以优化移动健康干预措施的信息传递,显著减少久坐不动的行为. 该方法为行为科学研究提供了可解释的见解.
科学领域:
- 数字健康数字健康
- 行为科学 行为科学
- 机器学习 机器学习
背景情况:
- 移动健康应用程序使用基于消息的干预来促进健康的行为.
- 目前的方法,如HeartSteps,缺乏对消息传递的优化策略,可能会给用户带来压倒.
- 需要有效和可解释的政策来最大限度地发挥干预效益.
研究的目的:
- 制定一个有效和可解释的政策,以优化移动卫生干预中的信息传递.
- 通过改进干预策略来减少久坐不动的行为.
- 为未来的行为科学研究提供见解.
主要方法:
- 拟议的最佳政策树 + (OPT+),一个批量政策之外的学习方法.
- 结合个性化门学习与最佳政策树的扩展.
- 使用HeartSteps V2/V3体育活动干预的数据测试了该方法.
主要成果:
- 显著减少了久坐不动的行为.
- 通过更低的消息传递预算实现了这些减少.
- OPT+生成了一个高度可解释和稳定的决策树.
结论:
- OPT+为优化移动卫生干预提供了一种有效和可解释的方法.
- 该方法可以在管理消息频率的同时显著减少久坐的行为.
- 可解释的政策为未来的数字健康和行为变化研究提供了理论见解.
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