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虫害战争:人工智能在败血症管理中进行了反击
Georgios I Barkas1, Ilias E Dimeas2, Ourania S Kotsiou1,2
1Laboratory of Human Pathophysiology, Department of Nursing, School of Health Sciences, University of Thessaly, 41500 Larissa, Greece.
Diagnostics (Basel, Switzerland)
|August 14, 2025
概括
人工智能 (AI) 和机器学习 (ML) 在通过早期检测和个性化抗生素管理来改善败血症护理方面表现有前途. 需要进一步的研究来验证现实世界和道德实施,以最大限度地提高临床影响.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 临床决策支持系统 临床决策支持系统
背景情况:
- 败血症是全球主要的健康挑战,导致高死亡率.
- 诊断延迟和过度使用抗生素有助于患者的不良结果和抗菌素耐药性.
- 目前的败血症管理需要创新的解决方案,以早期检测和优化治疗.
研究的目的:
- 系统地审查人工智能 (AI) 和机器学习 (ML) 在败血症护理中的应用.
- 评估AI/ML用于早期检测,个性化抗生素管理和抗菌素耐药性预测.
- 确定AI/ML在败血症管理中的挑战和未来方向.
主要方法:
- 从2019年到2025年对文献进行系统范围审查.
- 遵守PRISMA-ScR (系统性审查的首选报告项目和范围审查的元分析扩展) 准则.
- 使用乔安娜·布里格斯研究所 (JBI) 和QUADAS-2工具进行质量评估.
主要成果:
- 人工智能/ML模型在早期毒症检测方面表现出强大的预测性能 (AUROC 0.68-0.99).
- 像InSight和KI.SEP这样的AI工具利用多式联络数据进行实时临床支持.
- 人工智能平台在减少不适当的抗生素使用和改善患者结果方面显示出潜力.
结论:
- 人工智能和机器学习为彻底改变败血症护理提供了巨大的潜力,提高了早期检测和治疗.
- 挑战包括数据集成,算法偏差,可解释性以及对现实世界的验证的需求.
- 未来的努力必须集中在多中心验证,无的临床工作流集成和有效部署的伦理框架上.
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