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Ramtin Zargari Marandi¹, Frederik Boetius Hertz^2,3, Jesper Qvist Thomassen⁴

¹Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital, Copenhagen , Denmark.

Scientific reports

|May 20, 2025

概括

使用生物化学数据的机器学习模型可以帮助早期检测血流感染 (BSI). 最好的模型达到69%的AUC,在识别没有BSI的患者方面表现出色.

科学领域:

医疗信息学医疗信息学
临床微生物学临床微生物学
医疗保健中的机器学习

背景情况:

血流感染 (BSI) 的早期诊断对于适当的抗生素管理至关重要.
当前的诊断方法可能在速度和可访问性方面存在限制.
生物化学变量为快速BSI风险评估提供了一个潜在的途径.

研究的目的:

开发和评估一种机器学习 (ML) 模型,用于使用常规生化数据早期检测血液感染 (BSI).
为了确定BSI的关键生化预测因素.
评估模型在一个大规模的现实世界临床数据集中的表现.

主要方法:

来自丹麦Rigshospitalet的144,398名患者样本 (2010-2020年) 的回顾性分析.
开发7个ML模型,包括LightGBM,使用人口和多达36个生化变量.
在20%的数据集 (10,837个样本) 上对表现最好的模型进行独立测试.
利用夏普利添加式解释 (SHAP) 进行特征解释.

主要成果:

在独立测试组中,LightGBM模型实现了0.69的曲线下面积 (AUC).
该模型显示高负预测值 (NPV) 为0.96和特异性为0.74,表明在识别没有BSI的患者方面表现强.
最重要的预测特征包括血小板,白细胞和中性粒细胞与淋巴细胞的比率.
对大肠杆菌等常见病原体的敏感度为0.71,对常见BSI病原体的平均敏感度为0.66.

结论:

生物化学变量具有重要的潜力,可以作为早期血液感染检测的诊断因素.
开发的ML模型可以帮助临床医生识别BSI风险较低的患者,从而有可能优化抗生素的使用.
进一步的研究和将其整合到临床工作流中可以增强BSI管理策略.

关键词:

人工智能的人工智能是人工智能.生物标志物生物标志物分类模型的分类模型.临床实用性的临床实用性诊断诊断诊断诊断诊断电子医疗记录电子医疗记录感染管理感染管理可以解释性解释性现实世界的数据数据.

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