儿童长期COVID的基于机器学习的表型:来自RECOVER计划的基于EHR的研究.
Vitaly Lorman1, Hanieh Razzaghi1, Xing Song2
1Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
PloS one
|August 10, 2023
概括
一个新的机器学习算法可靠地在电子健康记录中识别了SARS CoV-2 (PASC) 的儿科后急性后续. 该工具有助于理解PASC,并通过准确分类患有或没有PASC的患者来支持临床试验招募.
科学领域:
- 儿科健康信息学 儿科健康信息学
- 计算流行病学计算流行病学
- 机器学习在医学中的应用
背景情况:
- 对于SARS CoV-2 (PASC) 的儿科后急性后续的临床理解和定义正在不断发展.
- 在卫生系统数据中可靠地识别PASC患者对于研究和临床护理至关重要.
- 现有的诊断代码可能无法完全捕捉儿童PASC的频谱.
研究的目的:
- 开发和验证一种机器学习算法,用于在电子健康记录 (EHR) 中识别儿科PASC患者.
- 区分儿童多系统炎症综合征 (MIS-C) 和PASC的非MIS-C变体.
- 通过使用多种EHR特征来描述PASC,并了解贡献因素.
主要方法:
- 分析了来自PEDSnet EHR网络的阳性SARS-CoV-2测试的儿科患者队列.
- 开发了一个XGBoost机器学习模型,利用条件,程序,诊断测试和药物作为功能.
- 特性选择采用基于树的扫描统计数据;模型性能使用5倍交叉验证和SHAP值进行验证.
主要成果:
- 创建了一个验证的机器学习模型来对儿科PASC患者进行分类.
- 该模型有效地利用EHR数据,包括诊断,药物,实验室和程序,用于PASC识别.
- SHAP值为PASC分类提供了对特征重要性的见解.
结论:
- 开发的机器学习算法为在卫生系统数据中识别儿科PASC提供了可靠的工具.
- 该模型支持用于研究的精确患者识别和用于临床试验的有效查.
- 对模型特征的分析可以提高对儿童PASC特征和相关因素的理解.
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