提高calprotectin的预测能力作为性结肠炎内学活动的生物标志物:机器学习使用案例
Mihaela Dranga1,2, Cătălina Mihai1,2, Otilia Gavrilescu1,2
1Internal Medicine Department, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania.
机器学习模型将便calprotectin与临床数据相结合,显著改善了在性结肠炎 (UC) 中内镜疾病活性的非侵入性预测. 这种方法提高了准确性,以便更好地管理患者.
科学领域:
- 胃肠病学 胃肠病学
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
背景情况:
- 由于疾病的爆发和缓解,性结肠炎 (UC) 的管理具有挑战性.
- 目前的疾病监测依赖于侵入性内镜,导致患者的不适和风险.
- 需要使用非侵入性方法来准确评估UC的疾病活性.
研究的目的:
- 为了增强内镜疾病活动 (EDA) 的非侵入性预测UC.
- 评估机器学习 (ML) 模型的性能,将便calprotectin和EDA预测的临床数据集成在一起.
- 为了证明使用综合数据方法比单独使用便calprotectin提高准确性.
主要方法:
- 一项前性,观察性,单中心研究,共103名UC患者.
- 多层感知子 (MLP) 模型被用作核心ML算法.
- 使用R.的caret库进行的变量重要性分析.
主要成果:
- 便calprotectin单独实现了70%的准确性和0.68的AUC.
- 结合便calprotectin和选定的临床预测因素的ML模型改善了预测.
- 最终的ML模型在测试组中实现了85%的准确性和0.93 AUC.
结论:
- 整合便calprotectin和临床数据的机器学习模型显著改善了UC内镜疾病活动的非侵入性预测.
- 这种方法为临床医生和患者在管理长期UC治疗方面提供了宝贵的工具.
- 这项研究开创了使用ML与便calprotectin用于估计UC内镜疾病活性.
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