时间变化的医疗数据的多模式融合模型:在序列融合中解决长期依赖和记忆挑战
Moxuan Ma1, Muyu Wang1, Lan Wei2
1School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing 100069, China.
Journal of biomedical informatics
|April 6, 2025
概括
本研究引入了一种新型的融合模型 (MMF-LD),有效地整合各种患者数据,包括长文本,以改善疾病进展分析和长期依赖性捕获.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数据融合技术 数据融合技术
背景情况:
- 患者住院治疗产生了反映疾病进展的多模式时间变化的数据.
- 当前的融合模型与长期状态扎,并且经常忽略诸如病房笔记之类的文本数据.
- 需要模型能够有效处理长序列和长文本.
研究的目的:
- 开发一个有效的医疗多模式时间变化的数据融合模型.
- 从长序列和长文本中提取特征.
- 为了捕捉患者数据中的长期依赖.
主要方法:
- 提出了长期依赖 (MMF-LD) 医疗多模式融合模型.
- 引入了一种渐进式多模式融合 (PMF) 策略,以防止在变化时间的文本融合中丢失信息.
- 集成的注意力机制与长短期存储内存 (LSTsM) 和时间卷积网络 (TCN) 进行增强的特征提取.
主要成果:
- 在急性心肌梗塞 (AMI) 和中风数据集上,MMF-LD表现出比现有模型更好的性能.
- 在死亡率和停留时间预测方面实现了高AUROC (高达0.965) 和AUPRC (高达0.675).
- 废弃性研究证实了PMF,LSTsM和TCN模块的贡献.
结论:
- 货币货币基金-LD架构有效地解决了将长文本信息纳入时间序列融合的挑战.
- 该模型在多个数据集和预测任务中表现出稳定的性能.
- 它在捕捉对患者护理至关重要的长期依赖方面表现出显著的优势.
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