实验室数据和IBDQ-有效预测器用于基于非侵入性机器学习的预测,在性结肠炎的内学活动
Otilia Gavrilescu1,2, Iolanda Valentina Popa3, Mihaela Dranga1,2
1Medicale I Department, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania.
Journal of clinical medicine
|June 10, 2023
概括
研究人员使用炎症性肠病问卷 (IBDQ) 开发了一种机器学习模型,以非侵入性地估计性结肠炎 (UC) 疾病的活动. 这种具有成本效益的方法可以提高准确性,从而改善患者管理.
科学领域:
- 胃肠病学 胃肠病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 在性结肠炎 (UC) 中非侵入性地评估内镜疾病活性 (EDA) 仍然是一个挑战.
- 目前的方法通常需要侵入性程序,限制频繁的监测.
研究的目的:
- 开发一个具有成本效益的,非侵入性机器学习 (ML) 模型来估计UC EDA.
- 评估炎症性肠病问卷 (IBDQ) 评分作为ML框架内的预测指标的有用性.
主要方法:
- 提出了四个随机森林 (RF) 和四个多层感知子 (MLP) 分类器.
- 利用IBDQ分数和低成本的生物预测器来训练和测试ML模型.
- 在独立患者队列上验证模型性能.
主要成果:
- 包括IBDQ得分显著提高了RF和MLP模型的准确性和AUC.
- 与MLP相比,RF技术在未见数据上表现出更高的性能.
- 这项研究是第一个将IBDQ纳入使用ML的UC EDA估计的预测因子.
结论:
- 开发的ML模型为评估UC EDA提供了一个有希望的非侵入性工具.
- 纳入IBDQ可以提高基于ML的EDA估计的准确性.
- 这种方法可以为临床医生和患者提供宝贵的见解,有助于长期的UC管理.
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