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使用可解释机器学习在ICU中早期预测败血症诱导的凝血病:一个多中心的回顾性队列研究

Tao Sha¹, Hao Jiang¹, Lei Feng¹

¹Department of Emergency, Huadong hospital, Fudan University, Shanghai, China.

Frontiers in medicine

|November 21, 2025

概括

这项研究开发了一种可解释的机器学习模型,用于预测ICU患者的败血症诱导凝血病 (SIC). 该模型准确地识别了早期干预的高风险患者.

科学领域:

关键护理医学关键护理医学
医疗保健中的机器学习
凝血障碍凝血障碍凝血障碍

背景情况:

败血症引起的凝血病 (SIC) 是重症监护室 (ICU) 患者的严重并发症.
早期预测SIC仍然是一个重大的临床挑战.
开发准确的预测模型对于及时干预至关重要.

研究的目的:

开发和验证一个可解释的机器学习模型,用于在ICU入院后七天内预测SIC.
确定与SIC发展相关的关键临床变量.
创建一个用户友好的工具,用于临床应用.

主要方法:

使用MIMIC-IV数据库进行模型开发,并使用eICU-CRD数据库进行外部验证.
采用了特征选择技术,包括LASSO,RF-RFE和Boruta.
通过5倍交叉验证训练和评估了10个机器学习模型,其中LightGBM被认为是最佳的.

主要成果:

一个包含13个变量的LightGBM模型实现了0.885 (内部) 和0.831 (外部) 的AUROC.
关键预测因素包括INR,血小板计数,SOFA得分,乳酸,SBP,RDW,碳酸盐,酸盐,血红蛋白,年龄,高血压,IHD和CRRT.
该模型被部署为一个交互式Web应用程序.

结论:

开发的机器学习模型显示出强大的预测性能和可用于早期SIC识别的可解释性.
这种工具可以帮助临床医生为患有SIC风险的患者实施有针对性的干预措施.
可访问的网络应用程序有助于临床采用和改善患者的治疗结果.

关键词:

这是一个MIMIC-IV数据库.早期预测早期预测机器学习是机器学习.预测模型的预测模型.败血症诱导的凝血病症

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