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基于机器学习,预测与HBV相关的急性-慢性肝衰竭的胃肠道出血

Jiwei Fu¹, Ahao Wu², Ziwei Zhou¹

¹Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.

Frontiers in medicine

|December 11, 2025

概括

胃肠道出血 (GIB) 在乙型肝炎病毒相关的急性至慢性肝衰竭 (HBV-ACLF) 患者中显著降低了短期生存率. 机器学习模型,特别是随机森林模型,显示了GIB强大的预测能力,有助于早期临床干预.

科学领域:

肝病学肝病学是一种肝病学.
胃肠病学胃肠病学
医疗信息学医疗信息学

背景情况:

与乙型肝炎病毒相关的急性至慢性肝衰竭 (HBV-ACLF) 是一种严重的临床疾病.
胃肠道出血 (GIB) 是HBV-ACLF患者的常见并发症.
在HBV-ACLF中,GIB对短期生存的影响仍然是一个关键问题.

研究的目的:

调查GIB对HBV-ACLF患者短期生存的影响.
开发和比较机器学习 (ML) 模型来预测HBV-ACLF中的GIB.
在这个患者群体中确定GIB的关键预测因子.

主要方法:

来自两个医疗中心的583名HBV-ACLF患者的回顾性分析.
使用最小绝对收缩和选择操作员 (LASSO) 回归来进行特征选择.
开发和评估了五种ML模型:物流回归 (LR),支持矢量机 (SVM),决策树 (DT),随机森林 (RF) 和K-最近邻居 (KNN).

主要成果:

与非GIB患者相比,GIB患者在所有队列中表现出明显较低的30日和90天生存率.
拉索确定了七个与GIB相关的特征,包括门高血压和电解质干扰.
随机森林 (RF) 模型在训练,测试和验证队列中展示了最强大和最一致的预测性能.

结论:

胃肠道出血 (GIB) 是HBV-ACLF的短期生存的一个显著的负预后因素.
机器学习模型,特别是射频模型,为预测HBV-ACLF患者的GIB提供了一个有希望的方法.
早期预测GIB可以促进及时的临床干预,可能改善患者的治疗结果.

关键词:

急性-慢性肝功能衰竭肝功能衰竭慢性乙型肝炎慢性乙型肝炎胃肠道出血胃肠道出血机器学习是机器学习.幸存率幸存率生存率

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