人工智能是否准备好应对ICU的24小时班次?
Filipe André Gonzalez1, Cristina Santonocito2, Tomás Lamas3
1Intensive Care Department in Hospital Garcia de Orta, Almada, Portugal; ICU in Hospital CUF Tejo, Lisboa, Portugal; Cardiovascular Research Center, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal.
Anaesthesia, critical care & pain medicine
|October 5, 2024
概括
在重症监护室 (ICU) 的机器学习 (ML) 通过分析数据以获得预测性见解来提高患者护理和效率. 挑战包括数据集成,模型可解释性和广泛采用的伦理问题.
科学领域:
- 关键护理医学 关键护理医学
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
背景情况:
- 重症监护病房 (ICU) 从电子健康记录和监测系统中产生大量数据.
- 机器学习 (ML) 提供了分析这些数据以改善患者护理和运营效率的潜力.
研究的目的:
- 审查将人工智能 (AI) 和ML工具集成到ICU环境中的挑战和机会.
- 引导有兴趣采用人工智能驱动的临床实践的临床医生.
主要方法:
- 讨论适用于ICU的主要类别的ML算法.
- 从数据启用到ML模型开发的过程概述.
- 探索患者分层和个性化护理的决策支持系统.
主要成果:
- ML在预测患者病情恶化方面表现出高度准确性,有助于早期干预和降低死亡率.
- 在败血症检测,呼吸机优化和资源配置方面,ML的潜力.
- 确定的挑战包括数据异质性,工作流集成,模型透明度和道德考虑.
结论:
- 人工智能和机器学习对提高ICU患者的治疗结果和医疗保健效率具有显著的前景.
- 解决与数据,集成,透明度,隐私和道德相关的挑战对于成功部署至关重要.
- 需要进一步的研究和仔细的实施,才能充分发挥ML在重症监护中的潜力.
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