使用程序和调度数据确定程序前禁食警报时间
Litong Zheng1, J Christopher Beck1, Sebastian Mafeld2
1Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
Health informatics journal
|May 9, 2024
概括
使用机器学习优化手术前禁食指南,显著减少了患者禁食时间. 这种方法确保患者满足最低禁食要求,同时尽量减少麻醉前不必要的长时间禁食.
科学领域:
- 麻醉学 麻醉学
- 医疗信息学 医疗信息学
- 医疗保健操作 医疗保健操作
背景情况:
- 标准的麻醉前禁食协议往往导致持续时间超过最低建议.
- 程序开始时间的不确定性导致患者长时间禁食.
研究的目的:
- 将标准禁食时间与使用机器学习 (ML) 和模拟的新方法进行比较.
- 为了优化预程序禁食时间点的计算.
主要方法:
- 使用加拿大学术医院的调度数据 (2020年1月至2022年4月) 进行的回顾性观察研究.
- 采用了四种基于ML和模拟的方法来预测程序开始时间.
- 基于树的ML模型被用来确定最佳的禁食指令时间.
主要成果:
- 标准实践中,对于食物和透明液体,平均禁食时间为10.08小时.
- 最好的ML方法将中位数的禁食时间减少到7.7小时 (食物/不清晰) 和3.7小时 (清晰液体).
- 97.3%的患者在优化方法下达到最低禁食要求.
结论:
- 机器学习模型可以有效地预测手术开始时间,以优化麻醉前禁食.
- 优化的禁食协议可以减少患者禁食的持续时间,同时保持安全标准.
- 需要进一步的研究来实施自动化禁食预警系统.
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