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人工智能和机器学习模型用于预测重症监护病房死亡率的预测性能:系统性审查

Archana Dhami¹, Kosisochukwu A Onyeukwu², Saba Sattar³

¹Family Medicine, Avalon University School of Medicine, Willemstad, CUW.

|September 22, 2025

概括

与传统的评分系统相比,人工智能和机器学习模型在预测重症监护室 (ICU) 患者死亡率方面表现优越. 这些先进的模型提供了早期风险分层,改善了重症监护决策.

科学领域:

关键护理医学关键护理医学
医疗信息学医学信息学
数据科学是数据科学.

背景情况:

在重症监护室 (ICU) 预测住院死亡率是一个重大的临床挑战.
现有的临床评分系统在准确性和及时性方面存在局限性.

研究的目的:

系统地审查人工智能 (AI) 和机器学习 (ML) 模型的应用和性能,以预测ICU患者的住院死亡率.
将AI/ML模型的疗效与传统的临床评分系统进行比较.

主要方法:

按照PRISMA指南进行系统审查,分析了15项研究 (2015年1月至2025年4月).
专注于使用MIMIC和eICU-CRD数据库的AI/ML算法 (XGBoost,随机森林,后勤回归,深度学习).
预测特征和模型性能的评估.

主要成果:

在死亡率预测方面,AI/ML模型的表现始终优于传统系统 (APACHE,SOFA,SAPS).
集合方法 (XGBoost,随机森林) 和深度学习显示出高准确度.
关键预测因素包括年龄,生命体征,实验室值和神经状态.
使用前24小时数据的模型证明了早期风险分层的可行性.

结论:

人工智能和机器学习模型具有显著的临床潜力,可以提高ICU死亡率的预测.
未来的研究应该专注于未来的验证,标准化和临床实施.
这些技术有可能改善决策和重症监护中的患者结果.

关键词:

人工智能的人工智能是人工智能.这就是CUCUCU.关键护理关键护理的关键护理这是一个ICU.重症监护室的重症监护室是重症监护室的重症监护室.机器学习是机器学习.死亡率死亡率死亡率死亡率预测死亡率预测

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