格鲁电视台 (GRU-TV):时间和速度意识的门式循环单位,用于患者代表
Ningtao Liu1, Shuiping Gou2, Ruoxi Gao3
1Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi'an, 710071, Shaanxi, China; Robarts Research Institute, Western University, London, N6A 5B7, ON, Canada.
Journal of biomedical informatics
|June 6, 2025
概括
这项研究引入了一种新型模型,可以捕捉患者瞬间的生理变化. 时间和速度感知门式循环单元 (GRU-TV) 模型改善了从电子健康记录中学习患者的代表性.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的机器学习
- 临床数据分析 临床数据分析
背景情况:
- 来自电子健康记录 (EHR) 的多变量临床时间序列 (MCTS) 对于理解动态生理过程至关重要.
- 现有的MCTS深度学习模型经常与数据归算和不规则的抽样作斗争.
- 在患者代表中,生理状态变化的瞬间速度在很大程度上被忽视了.
研究的目的:
- 解决MCTS中代表瞬间生理变化的差距.
- 开发一种新的深度学习模型,用于患者表示学习,并考虑时间动态和速度.
- 为了提高从EHR数据中描述患者状态的准确性.
主要方法:
- 提出了一个时间和速度感知的门式循环单元 (GRU-TV) 模型.
- 利用神经常规微分方程来建模即时的生理速度.
- 集成即时速度到隐藏状态更新和GRU模型的向前传播,以处理不均的时间间隔和不均的变化.
主要成果:
- 在现实数据集 (PhysioNet2012和MIMIC-III) 上,GRU-TV模型在多个临床任务中表现出强的性能.
- 在子任务中获得高平均AUC分数 (例如,在完整的PhysioNet2012数据上达到0.89) 和表型分类 (例如,在完整的MIMIC-III数据上达到0.84).
- 即使采用了大量数据采样 (例如,在PhysioNet2012上为50%,在MIMIC-III上为10%) 也表现出了稳定性,并且在停留时间预测中实现了1.84天的平均绝对偏差.
结论:
- 这些发现强调了即时生理变化在患者表现中的关键作用.
- 格鲁-TV模型在临床决策中提供了显著的进步,特别是在不完整或不规则地采样的EHR数据的情况下.
- 整合速度意识提高了模拟复杂生理动态的能力,以获得更好的患者护理见解.
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