预测SARS-CoV-2感染患者的存活率的入院血液测试:在不平衡数据集中的图形卷积网络的实际实施
Jie Lian1, Fan Huang1, Xinhai Huang2
1Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
BMC infectious diseases
|August 9, 2024
概括
图形卷积网络 (GCN) 准确预测COVID-19患者的生存率,在不平衡的数据集上表现优于传统模型. 这种方法提高了对资源优化的预测准确性.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 流行病学 流行病学
背景情况:
- 预测COVID-19死亡率对于资源分配至关重要,但像香港这样的数据集中的低事件率对传统模型构成挑战.
- 在COVID-19生存数据中的阶级不平衡阻碍了准确的预测工具的开发.
研究的目的:
- 引入和评估一种新的图形卷积网络 (GCN) 模型,用于预测COVID-19患者的生存率.
- 用GCN框架来解决COVID-19生存预测中阶级不平衡的挑战.
主要方法:
- 基于人口的GCN模型是使用来自香港7606名COVID-19患者 (2020年1月至12月) 的人口统计和实验室数据开发的.
- 将GCN模型的性能与考克斯比例危险 (CPH) 模型,传统机器学习算法和过量采样技术进行了比较.
- 进行了子组分析,以了解患者节点关系和预测不准确性的来源.
主要成果:
- GCN模型以0.944的曲线下面积 (AUC) 实现了最高的性能,明显超过所有其他模型 (p < 0.05).
- 卡普兰-梅尔估计证实了GCN模型在低风险和高风险个体之间进行歧视的能力 (p < 0.0001).
- 虽然总体上有效,但子组分析表明,在区分虚假负数和真负数的情况下存在局限性.
结论:
- 与现有的机器学习和CPH模型相比,GCN模型在COVID-19生存预测方面表现出卓越的性能.
- 使用人口图形表示与GCNs提供了一个有希望的方法,用于在不平衡的COVID-19生存数据集中准确预测.
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