从早期临床数据中基于人工智能识别长期COVID的关键风险因素
Jacobo Alonso-Domínguez1, José A González-Nóvoa2, Aida López-López1
1Virology and Pathogenesis, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Spain.
Journal of infection and public health
|November 16, 2025
概括
机器学习模型可以使用早期临床数据预测长期COVID风险,帮助早期干预. 关键预测因素包括血红蛋白和CRP,改善了持续的COVID后症状的患者管理.
科学领域:
- 生物医学信息学是生物医学信息学.
- 临床研究是临床研究.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 长期COVID在SARS-CoV-2感染后呈现持续症状,构成重大公共卫生挑战.
- 早期识别长期COVID风险的个体对于及时有效的管理策略至关重要.
- 这项研究调查了早期临床和生化标志物对长期COVID发展的预测潜力.
研究的目的:
- 开发和评估用于预测长期COVID风险的机器学习模型.
- 在入院后72小时内确定与长期COVID发展相关的关键临床和实验室生物标志物.
- 评估使用早期数据进行长期COVID风险分层的可行性.
主要方法:
- 对394名SARS-CoV-2患者的临床和实验室数据的分析.
- 使用XGBoost机器学习算法开发一个预测模型.
- 通过贝叶斯优化优化模型超参数,并使用SHAP值对变量重要性进行评估.
主要成果:
- 预测模型实现了0.732的AUC-ROC,表明了中等的歧视力.
- 关键的预测变量包括血红蛋白,氧和度,体重,CRP,APTT,,肺透和性别.
- 血红蛋白水平是唯一具有统计意义的预测因素 (p=0.015);模型召回率为78.6%.
结论:
- 机器学习模型可以使用早期临床和实验室数据预测长期COVID风险.
- 与单个标志物相比,结合生物标志物可以提高长期COVID风险评估.
- 需要进一步的研究来完善该模型并验证其在不同人群中的实用性,包括较温和的COVID-19病例.
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