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基于机器学习,预测急性至慢性肝衰竭患者的28天预后

Shaotian Qiu¹, Yumeng Zhao², Jiaxuan Hu¹

¹The School of Medicine, Nankai University, Tianjin 300071, China; Department of Gastroenterology and Hepatology, Tianjin Union Medical Center Affiliated to Nankai University, Tianjin 300121, China.

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|July 14, 2024

概括

我们开发了一种XGB-ML模型,用于预测急性至慢性肝衰竭 (ACLF) 患者的28天死亡率. 这种模型还有助于评估治疗的有效性,优于现有的得分.

科学领域:

肝病学肝病学是一种肝病学.
机器学习在医学中的应用
预测建模预测建模

背景情况:

急性至慢性肝衰竭 (ACLF) 在患者管理中构成了重大挑战.
准确预测28天死亡率对于及时干预和资源分配至关重要.
现有的预后分数可能无法完全捕捉到ACLF预后的复杂性.

研究的目的:

开发和验证一种机器学习 (ML) 模型,用于预测ACLF患者28天死亡率.
评估使用ML对ACLF医疗治疗的有效性.
将开发的ML模型的性能与已建立的临床评分进行比较.

主要方法:

分析了来自六家三级医院的736名ACLF患者队列.
拉索回归被用于特征选择,识别关键预测因素.
极端梯度提升 (XGB) 模型是使用AUC和精度指标开发和验证的.

主要成果:

与Child-Pugh,MELD和CLIF-SOFA得分相比,XGB-ML模型在预测28天死亡率方面表现优异.
确定的主要预测因素包括年龄,肝脏脑病变,总胆红素,前热血素活性和肌素.
顺序评估提高了预测准确度,有助于评估治疗有效性.

结论:

一个XGB-ML模型已成功建立,用于预测ACLF患者的28天死亡率.
该模型为评估ACLF的预后和指导治疗策略提供了有价值的工具.
XGB-ML模型提供了比传统评分系统更好的预测能力.

关键词:

急性-慢性肝功能衰竭肝功能衰竭肝功能衰竭机器学习机器学习预测预后预测预测

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