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对ICU再接收预测模型的全面审查:从统计方法到深度学习方法

Waleed Fathy¹, Guillaume Emeriaud², Farida Cheriet³

¹Department of Computer and Software Engineering, Polytechnique Montréal, Montreal, Quebec, Canada; Department of Electronic and Communication Engineering, Zagazig Univeristy, Zagazig, Sharkia, Egypt.

Artificial intelligence in medicine

|April 29, 2025

概括

预测重症监护室 (ICU) 再入院对于资源管理至关重要. 机器学习和深度学习模型显示中等成功,但临床决策支持工具需要进一步进步.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
关键护理医学关键护理医学

背景情况:

由于日益增加的资源需求,重症监护室 (ICU) 的再接收带来了挑战.
对重症患者的及时干预至关重要.
准确预测ICU再接收是一个关键的研究领域.

研究的目的:

提供统计,机器学习 (ML) 和深度学习 (DL) 模型的综合审查,用于ICU再接收预测.
讨论挑战,方法和该领域的最新进展.
确定研究缺口,并为未来的发展提供建议.

主要方法:

基于模型类型 (统计,ML,DL) 的现有研究的系统审查和分类.
评估模型的优点,局限性和性能指标.
探索当前的方法,数据使用,可解释性和可解释AI (XAI).

主要成果:

最近的ML和DL进步适度改善了ICU再接收风险预测.
现有的模型显示出希望,但需要进一步细化临床应用.
可解释性和XAI是影响模型开发的新兴领域.

结论:

虽然ML和DL提供了改进,但目前的预测精度不足以提供强大的临床决策支持工具.
建议进行进一步的研究,以提高预测准确性和临床实用性.
解决方法和数据使用中发现的差距对于未来的进步至关重要.

关键词:

深度学习是一种深度学习.在重症监护病房的重症监护病房.机器学习是机器学习.再接纳是指重新接纳的人.审查审查审查审查

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