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肝衰竭患者的凝血风险预测:综合元分析和机器学习模型研究

Hao Wang¹, Tao He¹, Liang Ren¹

¹Affiliated Hospital of North Sichuan Medical College, Nanchong, China.

JMIR medical informatics

|December 8, 2025

概括

人工肝脏支持系统 (ALSS) 在肝衰竭患者中显著改善凝血功能. 机器学习模型准确地预测了凝血功能障碍风险,有助于个性化管理.

科学领域:

肝病学和重症监护医学肝病学和重症监护医学
生物医学工程和人工器官开发
在医疗保健中的数据科学和机器学习.

背景情况:

肝衰竭经常导致严重的凝血功能障碍,这是一个重大的临床挑战.
人工肝脏支持系统 (ALSS) 在改善凝血参数方面表现有前途.
在ALSS中对凝血动态的预测建模仍未得到充分探索.

研究的目的:

评估ALSS对肝衰竭中的凝血功能的影响.
开发一个动态的机器学习模型来预测凝血参数的改进.
为了确定ALSS治疗期间凝血异常的关键预测因素.

主要方法:

18项研究 (1771名患者) 的系统文献综述和元分析.
评估ALSS对国际规范化比率 (INR),前列血时间 (PT),激活部分血栓形成时间 (APTT) 和纤维素原的影响.
使用ICU数据开发机器学习模型 (逻辑回归,XGBoost,随机森林,LSTM).

主要成果:

阿尔斯显著改善了INR,PT,APTT和纤维素原 (P<.05).
在不同的ALSS模式中,治疗疗效有所不同.
随机森林模型实现了最高的预测性能 (AUC 92.12%),动态INR作为关键预测因素.

结论:

在肝衰竭患者中,ALSS有效地改善了凝血参数,有效性因模式而异.
机器学习模型准确预测凝血功能障碍风险,支持早期识别和个性化管理.
该模型作为一个动态的风险评估工具,以帮助临床评估和护理干预.

关键词:

人工肝脏支持系统的人工肝脏支持系统凝固的功能是凝固的功能.肝功能衰竭肝功能衰竭肝功能衰竭机器学习是机器学习.这是一个元分析.预测建模预测建模

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