针对ICU患者预测结果的多式融合网络
Chutong Wang1, Xuebing Yang1, Mengxuan Sun1
1State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
概括
本研究引入了多式联接网络 (MFNet),用于使用电子健康记录 (EHR) 预测患者的结果. MFNet有效地整合了各种数据类型,以改善重症监护室 (ICU) 死亡率和停留时间 (LoS) 的预测.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床数据科学 临床数据科学
背景情况:
- 电子健康记录 (EHR) 庞大而异质,对利用各种数据模式提出了挑战.
- 在重症监护室 (ICU) 准确预测患者的结果对于有效的医疗管理至关重要.
研究的目的:
- 开发一个新的框架,即多式联接网络 (MFNet),用于从异质EHR数据中更好地预测患者的治疗结果.
- 为了应对在EHR中整合来自不同数据模式的信息的挑战.
主要方法:
- MFNet使用模式特定的编码器,包括用于医疗代码关系的图形引导编码器和预先微调的文本编码器.
- 一个层次的融合机制被用来偶联合并多式联接表示.
- 在eICU-CRD数据集上进行了实验.
主要成果:
- 与现有的基线方法相比,MFNet在预测死亡率和停留时间 (LoS) 中表现优异.
- 废弃性研究证实了MFNet框架内各组件的有效性.
结论:
- 拟议的MFNet框架有效地捕捉了EHR中不同数据模式之间的相关性.
- 在ICU患者预测结果方面,MFNet提供了显著的进步,提高了关键临床指标的准确性.
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