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五种算法方法和机器学习模式识别的比较,用于检测五种不同生命体征的电子记录中的文物:回顾性分析

Mathias Maleczek¹, Daniel Laxar¹, Lorenz Kapral²

¹Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, and Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.

|March 11, 2024

概括

没有一种单一的方法能够有效地检测出所有电子健康记录数据缺陷. 机器学习,特别是神经网络,有望改善特定生命体征中的文物检测,提高数据准确性.

科学领域:

生物医学信息学生物医学信息学
临床数据科学临床数据科学
医疗器械技术医疗器械技术

背景情况:

电子健康记录 (EHR) 的生理数据经常包含文物,影响数据完整性.
传统的文物检测方法通常依赖于简单的过器,这些过器可能并不普遍有效.
需要先进的算法,包括机器学习,被假设为最佳的文物检测在各种各样的生命体征和临床环境.

研究的目的:

评估各种文物检测算法的性能与EHR生理数据中的人类专家注释相比.
将经典文物检测方法与监督机器学习方法 (长短期记忆神经网络) 进行比较.
确定不同生命体征和临床背景 (手术室与重症监护室) 的最佳文物检测策略.

主要方法:

一项回顾性研究分析了心率,氧和,血压,温度和度的手术内和ICU EHR数据.
文物被至少两名人类专家识别出来,作为参考标准.
经典方法 (切断点,z值,IQR,LOF) 和长期短期记忆神经网络被测试了灵敏度和特异性.

主要成果:

文物检测算法显示出显著的性能差异;所有方法的特异性都超过了90%.
与经典方法相比,神经网络对心率 (ICU),侵入性血压 (OR/ICU) 和手术室温度表现出更高的灵敏度.
总体灵敏度一般较低,大多数方法低于80%,除了手术室中氧和的z值.

结论:

单一的文物检测方法在所有生命体征和临床环境中并不始终表现良好.
神经网络是一个有前途的途径,可以提高EHR数据中的特定生命体征的文物检测准确度.
对定制的文物检测算法进行进一步的研究是有必要的,以提高生理学数据的可靠性.

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