针对COVID-19结果预测的多模态图表关注网络
Matthias Keicher1, Hendrik Burwinkel2, David Bani-Harouni2
1Computer Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstr. 3, 85748, Garching, Germany. matthias.keicher@tum.de.
Scientific reports
|November 9, 2023
概括
这项研究引入了一种基于多模式图形的方法来预测COVID-19患者的结果,将成像和临床数据集成为更早的预后. 该方法准确预测重症监护室的入院,通风需求和死亡率,优于现有模型.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 计算生物学和生物信息学
- 传染病建模 传染病建模
背景情况:
- 预测个人COVID-19疾病进展是具有挑战性的,因为未知的患者和疾病特异性因素.
- 早期预测COVID-19患者的结果,如重症监护室 (ICU) 的入院,对于资源配置和治疗规划至关重要.
- 目前的方法通常依赖于急性指标,限制了及时和准确预测的能力.
研究的目的:
- 开发一种整体的,基于多模式图的方法来预测COVID-19患者的结果.
- 整合各种数据模式,包括成像 (CT扫描) 和非成像 (临床数据),以提供全面的患者代表性.
- 为了提高关键事件的早期预后,如ICU入院,通风和死亡率.
主要方法:
- 一个多式相似度指标被用来构建一个人口图表,使患者集群.
- 从胸部CT扫描中提取了放射性特征,使用分段神经网络,作为潜在图像特征编码器.
- 一个图表注意网络 (GAN) 集成的放射性和临床数据 (生命体征,人口统计数据,实验室结果) 用于端到端的结果预测.
主要成果:
- 基于多模式图表的方法在与单模式和非图表基线相比显示出更高的性能.
- 在图表中对患者进行聚类,可以了解患者关系和疾病进展模式.
- 图表注意力网络有效预测了COVID-19患者的关键结果,包括ICU入院,通风需求和死亡率.
结论:
- 拟议的基于多式联通图的方法为预测COVID-19患者结果提供了一个强大的框架.
- 通过图形网络整合成像和临床数据,提高了预测准确度,并提供了对患者队伍的更深入的理解.
- 这种方法有可能改善临床决策和新兴传染病资源管理.
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