基于常规实验室测试的慢性乙肝肝纤维化的机器学习诊断框架
1Medical Laboratory Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Medicine
|March 13, 2026
概括
机器学习模型可以有效地检测慢性乙型肝炎患者的肝纤维化. 随机森林模型显示为早期风险评估的个性化查工具的前景.
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
- 生物统计学 生物统计学
背景情况:
- 在慢性乙型肝炎 (CHB) 中早期发现肝纤维化对于患者的治疗结果至关重要.
- 精确的纤维化病期指导治疗决策和预后.
- 当前的诊断方法可能在灵敏度或可访问性方面存在局限性.
研究的目的:
- 开发和验证基于机器学习 (ML) 的模型,用于评估CHB患者的肝纤维化程度.
- 将ML模型的性能与传统评分系统进行比较.
- 建立一个个性化和动态的CHB相关肝纤维化风险评估工具.
主要方法:
- 分析了一组268名接受肝活检的CHB患者.
- 肝纤维化是使用Scheuer评分系统进行的.
- 使用最小绝对收缩和选择操作员回归来进行特征选择,然后对8个ML算法进行评估,包括随机森林 (RF).
主要成果:
- 随机森林 (RF) 模型表现得最好,曲线下的面积 (AUC) 为0.810 (训练集) 和0.793 (验证集).
- 决策曲线分析表明,RF模型在各种概率值中具有优越的净收益.
- 在30%的概率值下,RF模型获得了最高的灵敏度和净收益,超过了传统的得分.
结论:
- 机器学习模型,特别是射频,可以有效地评估CHB患者的肝纤维化.
- 开发的RF模型提供了一种安全,有效和个性化的查方法.
- 这种方法使得CHB患者的风险分层能够更早,更动态地进行分层.
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