时间到事件机器学习模型的比较,用于预测肝移植患者胆道并发症和死亡率
Aref Andishgar1, Sina Bazmi1, Kamran B Lankarani2
1USERN Office, Fasa University of Medical Sciences, Fasa, Iran.
Scientific reports
|February 8, 2025
概括
在肝移植 (LT) 后预测胆道并发症和死亡率至关重要. 机器学习模型确定了关键的风险因素,改善了LT接受者的患者结果和决策.
科学领域:
- 医疗信息学 医疗信息学
- 移植手术 移植手术
- 机器学习在医学中的应用
背景情况:
- 肝移植 (LT) 后的存活率低于最佳,胆道并发症 (BC) 显著导致死亡率.
- 确定BC和死亡率的预测因素对于改善患者的治疗结果至关重要.
研究的目的:
- 开发和评估用于预测胆道并发症 (BC) 和肝移植 (LT) 后死亡率的机器学习模型.
- 确定LT接受者的BC和死亡率的关键预测因素.
主要方法:
- 分析了1799个LT观测的纵向数据,其中包括40个预测结果的结果.
- 使用过器和嵌入式特征选择方法 (Cox-P,Cox-c,RSF,LASSO) 应用生存分析.
- 使用5倍交叉验证和超参数调整开发和验证了七个生存机器学习算法 (LASSO,Ridge,RSF,E-NET,GBS,C-GBS,FS-SVM).
主要成果:
- 使用Ridge模型的RSF实现了BC预测的C指数为0.699.
- 具有RSF模型的RSF在死亡率预测方面表现最高 (C指数:0.784).
- 对BC的关键预测因素包括LT移植类型,IBD,接受者BMI,PVT史和以前的LT. 死亡预测因素包括移植后的AST,肌素,接受者年龄,ALT和塔克罗利斯使用.
结论:
- 机器学习模型可以有效地预测肝移植后的胆道并发症和死亡率.
- 识别特定的风险因素可以增强临床决策,并可能改善肝移植接受者的结果.
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