机器学习的发作检测,预测和预测的现在和未来,包括对临床试验的未来影响
Wesley T Kerr1, Katherine N McFarlane1, Gabriela Figueiredo Pucci1
1Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
Frontiers in neurology
|July 26, 2024
概括
机器学习和人工智能可以改善发作的检测和预测,提高患者的护理. 严格的验证对于确认这些先进的神经诊断技术的好处至关重要.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 发作严重影响生活质量和死亡率.
- 准确的发作检测和预测是具有挑战性的临床问题.
- 区分型活动与其他神经症状至关重要.
研究的目的:
- 审查目前用于发作检测和预测的软件和硬件.
- 概述评估新的发作检测和预测技术的方法.
- 讨论这些技术对临床试验和患者护理的影响.
主要方法:
- 现有文献和技术的叙事审查.
- 分析机器学习和人工智能在神经诊断中的应用.
- 讨论使用电脑电图 (EEG) 和不使用电脑电图的长期监测技术.
主要成果:
- 目前的技术显示出高灵敏度,并减少了发作检测中的错误阳性.
- 机器学习和人工智能与神经诊断监测相结合,提供了有前途的方法.
- 长期监测,有或没有EEG,正在推进检测和预测能力.
结论:
- 使用人工智能和机器学习的发作检测和预测有可能改变病护理.
- 需要进一步验证以证明这些技术的临床益处和成本效益.
- 这些进展可能从根本上改变患者的临床管理.
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