在乙型肝炎病毒相关的肝硬化中,基于机器学习的减补预测
Hsueh-Chun Lin1, Meng-Lun Hsieh2, Meng-Yu Liu1
1Department of Health Services Administration, China Medical University, Taichung 406, Taiwan.
Diagnostics (Basel, Switzerland)
|November 13, 2025
概括
机器学习模型有效预测B型肝炎病毒 (HBV) 相关肝硬化患者的脱补偿. 抗病毒疗法是预测疾病进展和帮助临床决策的关键因素.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 减补剂显著增加了肝硬化患者的死亡率.
- 有限的研究存在于机器学习预测肝硬化失补偿.
- 与乙型肝炎病毒 (HBV) 相关的肝硬化严重需要预测模型.
研究的目的:
- 开发和验证用于预测HBV相关肝硬化并发症的机器学习模型.
- 评估各种算法对脱补偿事件的预测能力.
- 评估抗病毒疗法在预测肝硬化脱补偿方面的作用.
主要方法:
- 利用了50 047名HBV患者的电子健康记录.
- 训练并测试了32个机器学习模型 (SVM,LR,DT,RF) 用于预测水出血,,黄和多重并发症.
- 使用AUROC评估模型性能和准确性,考虑到恩特卡维尔 (ETV) 和拉米武丁 (LAM) 的使用.
主要成果:
- SVM和RF模型表现出强大的预测性能 (AUROCs 0.85-0.93,准确度 0.77-0.88) 对于,黄和多重并发症.
- 在ETV使用者中,SVM和LR显示出异位的优异差异 (AUROC 0.93,0.92).
- 抗病毒治疗细节和临床数据是脱补偿的重要预测因素.
结论:
- 机器学习模型可以帮助临床医生在肝硬化不补偿方面的决策.
- 电子健康记录是开发预测ML模型的宝贵资源.
- 抗病毒疗法是HBV相关肝硬化中脱补偿的关键预测因素.
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