多模式深度学习用于预测心力衰竭患者的住院死亡率,使用纵向胸部X射线和电子健康记录
Dengao Li1,2,3, Wen Xing4,5, Jumin Zhao6,5,7
1College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, 30 Yingze West Street, Taiyuan, 030024, Shanxi, China. lidengao@tyut.edu.cn.
The international journal of cardiovascular imaging
|January 9, 2025
概括
这项研究表明,使用人工智能将胸部X射线与电子健康记录结合起来,可以改善心力衰竭死亡率的预测. 这种多式联络方法提高了准确性,从而改善了患者的治疗结果.
科学领域:
- 人工智能在医学中的应用
- 心脏病学 心脏病学
- 医学成像分析 医学成像分析
背景情况:
- 心力衰竭是老年人群住院的主要原因,需要准确的死亡率预测.
- 目前的预测模型主要使用电子健康记录 (EHR),往往不充分利用胸部X射线 (CXR) 数据.
- 改善死亡率预测可以带来更好的患者结果和疾病管理.
研究的目的:
- 探索深度学习对胸部X射线 (CXR) 数据的使用,以预测心力衰竭患者的住院死亡率.
- 开发和评估一个新的多式联网深度学习网络,集成CXR和结构化EHR数据.
- 评估CXRs在心力衰竭死亡率预测中的附加预后价值.
主要方法:
- 提出了使用空间和时间脱变压器的多式联网 (MN-STDT).
- 综合纵向CXR和结构化的EHR数据用于预测建模.
- 在CheXpert上预训练空间编码器,然后在MIMIC-IV和MIMIC-CXR数据集上进行训练和评估.
主要成果:
- MN-STDT模型实现了最高的性能,AUC-ROC为0.8620.
- 多模式方法显著优于仅使用结构化数据 (AUC-ROC 0.8166) 或仅使用CXR数据 (AUC-ROC 0.7479) 的模型.
- SHAP分析为模型决策提供了可解释的见解.
结论:
- 纵向CXR数据,当与结构化的EHR数据相结合时,显著提高了心力衰竭死亡率预测的准确性.
- 开发的多式联机深度学习模型证明了CXRs的大量预后价值.
- 这种方法有可能改善心力衰竭管理中的临床决策支持.
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