一个基于机器学习的模型分析慢性乙型肝炎患者肝纤维化血清标志物
Congjie Zhang1, Zhenyu Shu2, Shanshan Chen3
1Center for Plastic & Reconstructive Surgery, Department of Dermatology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China.
Scientific reports
|May 27, 2024
概括
使用五个血清标记物的机器学习模型准确地预测慢性乙型肝炎 (CHB) 患者的肝纤维化阶段. 这种非侵入性方法有助于CHB的临床诊断和治疗监测.
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
- 生物标志物发现发现
背景情况:
- 精确的肝纤维化阶段确定对于治疗慢性乙型肝炎 (CHB) 至关重要.
- 目前的诊断方法可能具有侵入性或缺乏精度.
- 对于有效的纤维化评估,需要新的非侵入性生物标志物.
研究的目的:
- 为了确定血清标志物来预测CHB患者肝纤维化阶段.
- 开发和验证用于可靠纤维化分期的机器学习 (ML) 模型.
- 加强CHB的临床诊断和治疗策略.
主要方法:
- 从618名CHB患者 (培训队列) 的临床数据的回顾性分析.
- 使用mRMR和GBDT特征选择构建和比较六个ML模型 (逻辑回归,SVM,贝叶斯,KNN,DT,RF).
- 用571名CHB患者的数据对最佳ML模型进行外部验证.
主要成果:
- 选择了一个使用五种生物标志物 (HBV-DNA,血小板,血栓时间,INR,白蛋白) 的决策树 (DT) 模型.
- 在训练 (0.898-0.944) 和验证 (0.876-0.933) 队列中,DT模型实现了纤维化阶段 (F0-1,F2,F3,F4) 的高曲线下面面积 (AUC) 值.
- 模型的性能与病理诊断结果密切匹配,证明了准确的分类.
结论:
- 基于五个血清标记物的ML模型提供了在CHB患者中肝纤维化阶段的准确,非侵入性诊断.
- 这种模型可以显著帮助临床监测和治疗CHB.
- 开发的DT模型为早期和精确的纤维化评估提供了一个有前途的工具.
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