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机器学习见解:在TIPS安置后预测肝脏脑病变

Okan İnce¹, Hakan Önder², Mehmet Gençtürk³

¹Department of Radiology, Medical School, University of Minnesota, 420 Delaware Street S.E., Minneapolis, MN, 55455, USA. okan_ince@yahoo.com.

Cardiovascular and interventional radiology

|November 17, 2023

概括

机器学习模型可以预测经过跨内肝脏移植系统 (TIPS) 程序后的肝脏脑病变 (HE). 该工具有助于识别有风险的患者,以便更好地管理和选择.

科学领域:

肝病学肝病学是一种肝病学.
干预性放射学干预性放射学
机器学习在医学中的应用

背景情况:

跨内肝移植系统分流 (TIPS) 是一种用于管理门脉高血压并发症的程序.
肝脑病变 (HE) 是TIPS安置后的一个常见且严重的并发症.
预测TIPS后的HE对于患者的选择和管理至关重要.

研究的目的:

开发和评估机器学习 (ML) 模型,用于在TIPS安置后预测肝脑病变 (HE).
使用ML算法识别TIPS后的HE的关键预测因素.

主要方法:

对327名接受肝硬化TIPS治疗的患者进行了回顾性分析.
30个特征的集合 (临床,实验室,程序).
使用嵌套的五倍交叉验证开发和评估支持矢量机 (SVM),物流回归 (LR) 和CatBoost模型.

主要成果:

32%的患者在手术后出现了HE.
腹出血和高波多系统后压力梯度与增加的HE风险有关.
ML模型实现了高预测性能,AUC值高达0.83.

结论:

机器学习模型在识别TIPS后高血压风险患者方面显示出显著的潜力.
这些模型可以作为优化患者选择和术后管理的宝贵工具.
进一步将ML纳入临床实践可能会改善TIPS患者的治疗结果.

关键词:

肝脏脑病变肝脏脑病变是一种肝脏脑病变.机器学习是机器学习.港口系统的分流操作跨的状体.

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