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可解释的人工智能通过结血-炎症概况来解开败血症异质性,用于预后和分层
Li Zhu1,2, Zengtian Chen1, Hong Zhang3
1School of Information Engineering, Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications, Nanchang University, Nanchang, Jiangxi, China.
Nature communications
|November 24, 2025
概括
我们开发了人工智能模型SepsisFormer和风险工具SMART,以改善败血症预后和分层. 这些工具有助于识别患者的风险水平和亚现象,以获得更好的治疗,特别是在资源有限的环境中.
科学领域:
- 人工智能在医学中的应用
- 计算生物学 计算生物学
- 临床信息学 临床信息学
背景情况:
- 败血症是医院死亡的主要原因.
- 败血症异质性使患者的预后和风险分层复杂化.
- 现有的方法很难有效地管理败血症的复杂性.
研究的目的:
- 开发一种可解释的AI预后模型 (SepsisFormer) 来治疗败血症.
- 创建一个自动化风险分层工具 (SMART) 用于败血症管理.
- 在临床实践中解决败血症异质性的挑战.
主要方法:
- 开发了基于变压器的神经网络SepsisFormer,用于败血症预后.
- 创建了SMART,这是用于败血症风险分层的自动化工具.
- 进行了一项多中心回顾性研究,涉及12,408名败血症患者.
主要成果:
- 败血症Former表现出高的预测准确性 (AUC:0.9301).
- 智能工具超过了已有的评分系统 (AUC: 0.7360).
- 确定了七个例行实验室测量和年龄,用于分类四个风险级别和两个亚现象型 (CIS1,CIS2),具有不同的结果.
- 中度/重度风险或CIS2患者对抗凝血剂的益处更大.
结论:
- 败血症Former和SMART为管理败血症异质性提供了有效的工具.
- 这些工具可以增强全球性败血症临床实践,包括资源有限的设置.
- 风险分层和亚现象识别可以指导针对性的治疗,如抗凝药.
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