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早期败血症预测使用公开可用的数据:高性能AI/ML模型与第一小时临床信息

Hao Wang¹, Destiny Pounds², Wenhui Zhang³

¹Department of Emergency Medicine, JPS Health Network, 1500 S. Main St., Fort Worth, TX 76104, USA.

Diagnostics (Basel, Switzerland)

|November 13, 2025

概括

人工智能/ML模型使用早期临床数据准确预测败血症. XGBoost模型显示了实时败血症查的强大潜力,改善了患者的治疗结果.

科学领域:

人工智能的人工智能
机器学习机器学习
医疗信息学医疗信息学

背景情况:

早期的败血症鉴定对于降低发病率和死亡率至关重要.
延迟诊断显著恶化了患者的结果.
人工智能/ML为改善早期败血症检测提供了潜力.

研究的目的:

开发和验证AI/ML模型用于早期败血症预测.
使用结构化EHR数据,波形数据和组合数据源.
专注于最大限度地回忆,以便及时识别败血症.

主要方法:

使用CHoRUS数据集 (AIM-AHEAD60) 的回顾性观察研究.
包括成年患者,最终诊断为败血症.
提取了第一小时的EHR和波形数据;开发了XGBoost,LightGBM,HistGB模型.

主要成果:

XGBoost获得了最高的AUROC (0.922),回忆率高于80%.
关键预测因素包括乳糖,白细胞计数,呼吸率和血压趋势.
模型表现强,尽管缺少数据.

结论:

使用初始临床数据,用于早期败血症预测的高性能AI/ML模型是可行的.
XGBoost模型显示了实时临床败血症查的巨大潜力.
公共可用的数据集可以支持开发有效的败血症预测工具.

关键词:

人工智能/MLML 算法算法是一种算法.早期检测早期检测这是一种血症.

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