医疗保健应用程序的连续时间决策变压器
Zhiyue Zhang1, Hongyuan Mei2, Yanxun Xu1
1Johns Hopkins University.
概括
连续时间决策转换器通过结合访问时间来增强医疗保健的线下强化学习. 这种方法通过从历史数据中学习最佳治疗策略来改善患者的健康和生存率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗保健信息学 医疗保健信息学
背景情况:
- 离线强化学习 (RL) 对治疗政策学习和临床决策支持等医疗应用具有前景.
- 现有的决策转换器模型仅限于离散时间场景,阻碍其在连续医疗决策中使用.
研究的目的:
- 为了将决策转换器推广为持续的医疗决策.
- 开发一个模型,从历史医疗数据中学习最佳治疗计划和访问时间表.
主要方法:
- 将通用决策变压器转换为连续时间决策变压器 (CTDT).
- 将过去的临床测量,治疗和访问时间纳入模型.
- 训练有素的CTDT可以预测未来的诊断时间和治疗计划.
主要成果:
- 在对合成和模拟医疗数据集的实验中,CTDT的性能优于现有方法.
- 在改善患者健康结果和生存率方面,已证明临床效用.
- 成功地从不同质量的记录数据中学习了高性能策略.
结论:
- 连续时间决策变压器是连续时间医疗领域的线下RL的可行方法.
- CTDT为提高临床决策支持和患者护理提供了显著的潜力.
- 该模型有效地从历史医疗数据中学习,以优化治疗策略.
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