使用人工智能预测患者病情恶化的生理数据:系统审查协议
Lynsey Threlfall1,2, Cen Cong3, Victoria Riccalton3
1Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
BMJ health & care informatics
|August 5, 2025
概括
本系统性审查确定了分析生理数据以预测患者病情恶化的最佳人工智能 (AI) 算法. 目标是改善国家早期预警得分.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持系统 临床决策支持系统
- 医疗保健中的人工智能
背景情况:
- 国家早期预警分数 (NEWS2) 被广泛使用,但对患者病情恶化的预测准确性有限.
- 人工智能 (AI) 在预测临床衰退方面表现有前途,但生理数据分析的最佳算法仍然不清楚.
研究的目的:
- 系统地审查和识别最有效的AI和机器学习算法,用于分析生理数据,以预测患者在医院环境中的恶化.
主要方法:
- 按照PRISMA和PICOS框架进行系统审查.
- 从2007年到现在,对八个主要数据库 (PubMed,Embase,CINAHL,Cochrane,Web of Science,Scopus,IEEE Xplore,ACM数字图书馆) 进行了全面的搜索.
- 由两名审稿人进行独立的数据选和提取,并通过讨论解决差异.
主要成果:
- 本部分将详细介绍系统审查的结果,强调各种AI/机器学习算法的性能,以预测基于生理数据的患者病情恶化.
- 该审查将比较不同算法的有效性,分析复杂的生理数据集.
结论:
- 这些发现将指导选择最佳的人工智能算法,以加强早期检测患者病情恶化.
- 这项研究旨在通过在医疗保健中利用先进的人工智能来改善临床决策和患者的结果.
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