对EKFC方程和机器学习模型进行比较,以预测淋巴细胞过率.
Felipe Kenji Nakano1,2, Anna Åkesson3,4, Jasper de Boer5,6
1Department of Public Health and Primary Care, KU Leuven Campus Kulak Kortrijk, Kortrijk, Belgium. felipekenji.nakano@kuleuven.be.
Scientific reports
|November 2, 2024
概括
与欧洲功能联盟 (EKFC) 方程相比,机器学习 (ML) 模型在预测功能方面表现出竞争力. 随机森林 (RF) 在12岁以下的儿童中略高于EKFC的表现,这表明ML ML.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 淋巴膜过率 (GFR) 对于评估功能至关重要.
- 目前的GFR估计方程,如欧洲功能联盟 (EKFC),在准确性上有局限性.
- 机器学习 (ML) 提供了改善GFR预测的潜力.
研究的目的:
- 与EKFC方程相比,研究ML方法是否可以提高GFR预测的准确性.
- 进行一项大规模的多中心研究,比较ML和EKFC用于GFR估计.
- 用临床和人口统计特征评估各种ML模型.
主要方法:
- 使用了来自13个队列的19,629名患者的数据集.
- 训练了各种ML方法,包括随机森林 (RF),使用年龄,性别,肌素,囊素C,身高,体重和BMI等特征.
- 在内部和外部验证队列中,通过将ML预测与EKFC方程进行比较来评估性能.
主要成果:
- 随机森林 (RF) 模型在整个队列中展示了与EKFC方程相比的竞争性表现.
- 射频和EKFC实现了类似的P10和P30值,表明了可比的整体准确性.
- 射频在预测12岁以下患者的GFR方面比EKFC有轻微的性能优势.
结论:
- 机器学习模型,特别是射频模型,可以与已建立的EKFC方程竞争,用于GFR预测.
- ML显示了改善GFR估计的前景,特别是在特定的儿科群体.
- 需要在大型多中心环境中进行进一步的研究,以充分确定ML在科中的临床实用性.
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