用于利用生物标志物驱动的机器学习模型来预测COVID-19患者结果的计算和统计见解
Jitendra Mehta1, Sayani Das1, Vibhav Prakash Singh2
1Department of Applied Mechanics, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh India.
Health information science and systems
|January 9, 2026
概括
机器学习模型使用生物标志物数据准确预测COVID-19患者的生存率. 包括LightGBM在内的前五个模型确定了预测结果的关键生物标志物,有助于临床管理.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 在医疗保健中的数据科学.
背景情况:
- 随着COVID-19的爆发,人们越来越需要准确地预测患者的结果.
- 预测死亡率的现有方法需要提高准确性和及时性.
研究的目的:
- 评估14个机器学习 (ML) 模型,使用生物标志物数据预测COVID-19患者的生存率.
- 确定最有影响力的生物标志物和表现最好的ML模型,用于COVID-19结果预测.
主要方法:
- 使用26个临床生物标志物训练和评估了14个ML模型.
- 利用SHAP图表来识别15个关键生物标志物,并根据性能指标 (准确性,精度,回忆等) 排名模型. ) 的情况.
- 删除了影响力较小的生物标志物和模型,然后在两个患者队列上重新训练了前五个模型 (LightGBM,随机森林,CatBoost,XGBoost,梯度提升).
主要成果:
- 五个选择的ML模型准确地预测了不同人口统计学群体的患者存活率和非存活率.
- SHAP分析确定了15个有影响力的生物标志物,包括年龄和性别,这些生物标志物调节了患者的结果.
- 与其他评估的ML模型相比,LightGBM表现出卓越的性能.
结论:
- 识别的ML模型,特别是LightGBM,可以有效地使用生物标志物数据预测COVID-19患者的结果.
- 这些发现支持疾病监测,资源分配和临床管理危急的COVID-19患者.
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