基于树的分类模型用于长期COVID感染预测,使用国家COVID队列协作数据进行年龄分层
Will Ke Wang1, Hayoung Jeong1, Leeor Hershkovich1
1Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States.
JAMIA open
|November 11, 2024
概括
一种新的分类模型有效地使用电子健康记录诊断SARS-CoV-2感染 (PASC) 或长期COVID的急性后续症状. 这种以年龄分层,知识驱动的方法改善了不同患者群体的长期COVID诊断.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 公共卫生研究 公共卫生研究
背景情况:
- SARS-CoV-2 感染 (PASC) 或长期COVID的后急性连续症,由于其异质症状, presents一个复杂的诊断挑战.
- 电子健康记录 (EHR) 包含有价值的数据,但往往受到稀缺性的困扰,并且需要对PASC等疾病进行复杂的分析.
研究的目的:
- 提出和验证一个基于领域知识的分类模型,用于使用EHR数据诊断PASC.
- 开发一个可靠和可解释的模型,包括临床相关的特征和人口分层.
主要方法:
- 根据文献审查,开发了一个基于XGBoost树的分类模型,包含PASC和COVID-19严重程度的指示特征.
- 利用了来自长期COVID计算挑战 (L3C) 的数据,这是国家COVID队列协作 (N3C) 的子集.
- 微调和校准的模型,以获得最佳的接收机运行特征曲线下的区域 (AUROC) 和F1得分,解决N3C数据中的类不平衡.
主要成果:
- 在培训数据中,在年龄分层的人群中,平均5倍的交叉验证的AUROC达到0.844和F1得分为0.539.
- 在独立测试数据集上获得了0.814的整体AUROC和0.545的F1得分.
- 在不同数据集和年龄组中表现出强大的性能和通用性.
结论:
- 这项研究验证了基于年龄分层,基于树的分类模型在PASC诊断中的有效性.
- 以知识为导向的特征工程和人口分层对于从稀疏的EHR数据中诊断PASC等复杂疾病至关重要.
- 该模型的可解释性和稳定性为管理长期COVID的临床翻译提供了潜力.
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