临床数据和定量CT参数与机器学习相结合,预测老年人中严重COVID-19的短期预后
Lifang Fan1, Shujian Wu2, Yimin Wu3
1School of Medical Imageology, Wannan Medical College, Wuhu, Anhui, China.
Heliyon
|September 23, 2024
概括
整合临床数据和CT扫描的机器学习模型准确地预测老年患者严重COVID-19的短期结果. 决策树算法在本研究中显示出最高的预测准确性.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 肺病学和传染病的研究
- 老年医学 老年医学
背景情况:
- 在老年患者中,严重的COVID-19具有显著的短期死亡风险.
- 准确预测预后对于及时干预和资源分配至关重要.
- 整合不同的数据源可能会增强预测能力.
研究的目的:
- 评估结合临床数据与定量CT参数的有效性,使用机器学习来预测老年严重COVID-19患者的短期结果.
- 确定这一患者队列中死亡率的关键预测因素.
- 为了比较不同的机器学习算法的性能,用于预后建模.
主要方法:
- 对239名老年严重COVID-19患者的回顾性分析,分为生存和死亡组.
- 使用3D-Slicer软件提取了定量CT参数.
- 开发和验证了机器学习模型 (逻辑回归,随机森林,决策树,支持矢量机器).
主要成果:
- 年龄,hs-CRP和SOV%被确定为死亡率的独立预测因素.
- 决策树 (DT) 模型在培训 (0.854) 和验证 (0.869) 组中实现了最高的曲线下面积 (AUC).
- 在预测短期死亡率方面,DT模型的表现优于LR,RF和SVM.
结论:
- 将临床数据和定量CT参数与机器学习相结合,为预测老年严重COVID-19患者的短期预后提供了一种有价值的方法.
- 基于决策树算法的模型显示出对预后预测的卓越准确性和可靠性.
- 这种方法可以帮助临床决策管理严重的COVID-19在老年人中.
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