使用白细胞子集对炎症性肠病活动进行机器学习分类
Eleanor Lehman1,2, Peyton Briand1,2, Kyra Fine3
1School of Computing, Queen's University, Kingston, Ontario, Canada.
BMJ open gastroenterology
|November 28, 2025
概括
分析常规血液检查的机器学习模型可以有效地分类炎症性肠病 (IBD) 活动. 这种方法为跟踪IBD疾病活动提供了比目前的生物标志物更快,更准确的替代方案.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 胃肠病学 胃肠病学
背景情况:
- 目前的炎症性肠病 (IBD) 监测缺乏快速,验证的测试.
- 像便calprotectin这样的现有生物标志物是缓慢的,血液检查缺乏特异性.
- 白血细胞 (WBC) 子集与IBD活动之间的关系尚未研究.
研究的目的:
- 使用常规血液检查对IBD疾病活动进行分类.
- 应用机器学习 (ML) 来识别WBC子集中的复杂模式.
- 开发一种用于跟踪IBD活动的新方法.
主要方法:
- 分析了来自108名IBD患者的1458个血液测量结果.
- 疾病活动根据医生的总体评估进行分类.
- 四个ML模型训练了全血细胞计,差异,白蛋白,ESR和CRP.
主要成果:
- 最优的ML模型实现了0.882.2的AUC.
- 中性粒细胞,C反应蛋白和白蛋白是关键的预测特征.
- ML模型的表现优于单个生物标志物,药物的影响最小.
结论:
- 常规血液检查的ML分析可以有效地分类IBD活动.
- 这种方法可以帮助治疗规划,改善患者的治疗结果.
- 这种方法为当前IBD监测工具提供了更有效的替代方案.
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