我们有个性化吗? 通过使用重新采样的在线强化学习算法来评估个性化
Susobhan Ghosh1, Raphael Kim2, Prasidh Chhabria3
1Department of Computer Science, Harvard University.
概括
强化学习 (RL) 可以个性化数字健康治疗,但其有效性需要验证. 这项研究引入了一种方法来确认RL个性化是否真实,还是其固有的随机性的工件.
科学领域:
- 数字健康干预措施 数字健康干预措施
- 医疗保健中的机器学习
- 行为科学 行为科学
背景情况:
- 越来越多的人对使用强化学习 (RL) 进行个性化的数字健康治疗,以促进更健康的行为感兴趣.
- 数字健康中的顺序决策依赖于治疗时间和类型的用户环境.
- 在线RL提供了一种数据驱动的方法,以根据用户响应来个性化治疗.
研究的目的:
- 评估数据证据,证实RL算法真正为用户个性化治疗.
- 调查观察到的RL个性化是否是算法的随机性的一个工件.
- 引入基于重新抽样的方法来评估RL个性化.
主要方法:
- 为RL算法开发了个性化工作定义.
- 引入了基于重新抽样的方法来区分真正的个性化和算法随机性.
- 将该方法应用于HeartSteps体育活动临床试验中的数据.
主要成果:
- 展示了一种严格评估在线RL算法的个性化能力的方法.
- 案例研究说明了该方法如何增强对算法个性化的评估.
- 个性化在所有用户和个人用户数据中都得到了评估.
结论:
- 拟议的方法为数字健康中的RL个性化数据驱动的真相广告提供了一个框架.
- 验证RL个性化对于优化数字健康干预措施的伦理和有效部署至关重要.
- 这种方法有助于确保RL算法为行为变化提供有意义的,个性化的支持.
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