一个可预测的模型,用于功能治愈的慢性HBV患者治疗基化干醇α:基于临床数据的多个算法的比较研究
Ya-Mei Ye1, Yong Lin1, Fang Sun1
1Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, 350000, China.
使用基线和12周数据的预测模型可以预测在接受基化干扰素α (PEG-INFα) 治疗的慢性乙型肝炎患者中,乙型肝炎表面抗原 (HBsAg) 清除. 该模型有助于预测慢性乙型肝炎的治疗结果.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 病毒学 病毒学
- 机器学习在医学中的应用
背景情况:
- 慢性乙型肝炎 (CHB) 感染影响全球数百万人.
- 基化干扰素α (PEG-INFα) 是慢性心脏病的关键治疗方法.
- 预测HBsAg清除对于评估治疗疗效和实现功能治愈至关重要.
研究的目的:
- 开发和验证48周HBsAg清除的预测模型.
- 确定与接受PEG-INFα治疗的CHB患者的HBsAg清除相关的关键临床变量.
- 为了比较不同的机器学习算法的性能来预测HBsAg清除.
主要方法:
- 分析了接受PEG-INFα的224名CHB患者的队列.
- 拉索回归确定了预测变量.
- 后勤回归,随机森林,梯度提升,XGBoost和SVM模型被开发和比较.
- 用AUC,灵敏度,特异性和F1评分来评估模型性能.
主要成果:
- 对HBsAg清除的关键预测因素包括基线日志2 ((HBsAg),性别,年龄,12周中性粒细胞计数,12周HBsAg下降率,12周HBcAb.
- 后勤回归模型实现了0.858的AUC,超过了其他机器学习模型.
- 选择的模型表现出对HBsAg清除的良好预测准确性.
结论:
- 结合基线log2 ((HBsAg),第12周HBsAg下降率,性别,第12周中性粒细胞计数和年龄的预测模型可以有效预测HBsAg清除.
- 该模型为预测PEG-INFα治疗的CHB患者治疗反应提供了有价值的工具.
- 早期预测HBsAg清除可以指导临床管理并改善患者的治疗结果.
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