从药物输注历史中预测麻醉深度的深度学习框架
Mingjin Chen1, Yongkang He1, Zhijing Yang1
1School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
这项研究引入了一种深度学习框架,可以准确预测在propofol-remifentanil目标控制输注期间的麻醉深度. 这种新的方法改善了不同患者群体和麻醉阶段的预测.
科学领域:
- 麻醉学 麻醉学
- 人工智能的人工智能
- 药理学 药理学是指药理学的学科.
背景情况:
- 准确预测麻醉深度是挑战在目标控制输注 (TCI) 的propofol和remifentanil.
- 患者生理特征的变化导致不一致的药理动力学反应和不同的数据分布.
- 麻醉意识可能发生,特别是在年轻的成年人中,使麻醉深度 (DOA) 监测复杂化.
研究的目的:
- 开发一个深度学习框架,用于在TCI麻醉期间连续和准确地预测双光谱指数 (BIS).
- 为了解决药理动力学反应和数据分布差异的患者间变异性.
- 改进对所有麻醉阶段的麻醉深度 (DOA) 的预测.
主要方法:
- 采用了一个深度学习框架,包括域调整和知识蒸.
- 一个修改的自适应循环神经网络 (AdaRNN) 被用来处理不同的数据分布.
- 一个知识蒸管道训练了预测网络,使用来自教师网络的中间特征表示.
主要成果:
- 与现有方法相比,拟议的方法在所有麻醉阶段都表现出优越的性能.
- 在根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 中观察到显著的改善.
- 该方法在内部和公开可用的数据集上实现了最先进的性能.
结论:
- 深度学习框架有效地解决了在TCI期间预测麻醉深度的挑战.
- 域调整和知识蒸的整合提高了预测的准确性和稳定性.
- 这种方法为改善患者安全和麻醉管理提供了一个有希望的解决方案.
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