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使用机器学习预测临床缓解与专属肠道营养在儿科克罗恩病的临床缓解
Ricardo G Suarez Suarez1, Daniel G McClement1, Roberto Vega2
1Department of Paediatrics, University of Alberta, Edmonton, Alberta, Canada.
机器学习模型可以预测儿童克罗恩病患者接受排他性肠道营养 (EEN) 的治疗成功. 这些工具可以通过早期识别响应者,帮助个性化治疗.
科学领域:
- 儿科胃肠病学 儿科胃肠病学
- 计算生物学是一种计算生物学.
- 临床数据科学临床数据科学
背景情况:
- 独家肠道营养 (EEN) 是儿童克罗恩病 (pCD) 的首要治疗方法,以诱导缓解.
- 患者对EEN的反应各不相同,因此需要方法来预测治疗结果.
- 预测缓解对于定制PCD治疗策略至关重要.
研究的目的:
- 使用基线临床数据,开发儿童克罗恩病患者EEN后临床缓解的预测模型.
- 创建一个模型,预测在pCD中EEN诱导后持续的,无皮质类固醇缓解.
- 建立有助于精确管理儿科克罗恩病的工具.
主要方法:
- 机器学习算法应用于308名前性跟踪pCD患者的临床和实验室数据.
- 用特征选择和k-fold交叉验证 (内部和外部) 来优化预测模型.
- 模型性能通过外部交叉验证和排列标签测试来验证.
主要成果:
- 开发了两个不同的模型:一个用于EEN诱导的缓解 (n=114) 和一个用于持续的无皮质类固醇缓解 (n=206).
- 这些模型在0.65±0.015和0.60±0.018的曲线值下的外部面积下显示了预测效果.
- 变换试验证实了模型的稳定性和统计学意义 (p<0.05).
结论:
- 使用可访问的临床特征的机器学习模型可以有效地预测儿科克罗恩病的EEN治疗成功.
- 这些发现支持开发用于PCD精度治疗的临床决策支持工具.
- 这些模型为儿童克罗恩病患者个性化EEN治疗策略提供了一个有希望的方法.
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