对非侵入性血压估计机器学习方法的审查
Ravi Pal1, Joshua Le2, Akos Rudas3
1Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Ronald Reagan UCLA Medical Center, 757 Westwood Plaza, Los Angeles, CA, 90095, USA. RPal@mednet.ucla.edu.
Journal of clinical monitoring and computing
|September 21, 2024
概括
这篇评论探讨了用于非侵入性血压估计的机器学习. 这些先进的方法为持续的血液动力学监测提供了侵入性技术的更安全的替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 临床监测 临床监测
- 人工智能在医学中的应用
背景情况:
- 血压监测对于评估患者的血液动力学状况至关重要.
- 低血压和高血压显著增加患者的发病率.
- 血压监测的侵入性和非侵入性方法都存在,每个方法都有独特的风险-收益概况.
研究的目的:
- 审查基于机器学习 (ML) 的现代血压估计的非侵入性方法.
- 讨论这些ML驱动技术的优点和局限性.
- 评估非侵入性ML血压监测的临床相关性.
主要方法:
- 关于机器学习用于非侵入性血压估计的最新研究的文献综述.
- 分析不同的ML算法及其性能指标.
- 非侵入性ML方法与传统的侵入性和非侵入性技术的比较.
主要成果:
- 机器学习模型显示出准确,连续,非侵入性血压估计的前景.
- 与侵入性监测相比,非侵入性ML方法的风险较低.
- 当前的ML方法在标准化和广泛的临床采用方面存在挑战.
结论:
- 机器学习在非侵入性血压监测方面取得了重大进展.
- 需要进一步的研究和验证,以临床整合基于ML的BP估计.
- 非侵入性ML技术有可能彻底改变患者的血液动力学评估.
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