多模式电子健康记录的自动融合为更好的医疗预测提供了更好的医疗预测
Suhan Cui1, Jiaqi Wang1, Yuan Zhong1
1Pennsylvania State University.
Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining
|October 14, 2024
概括
AutoFM自动化了电子健康记录 (EHR) 的深度学习模型设计. 这种新型的神经架构搜索 (NAS) 框架优化了多式联络电子健康记录数据分析,改善了医疗保健洞察力.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 电子健康记录 (EHR) 系统产生了大量的医疗数据,为深度学习提供了机会.
- 现实世界EHR数据的复杂性和多模式性挑战了当前的深度学习模型设计.
- 现有的方法通常依赖于手动,基于直觉的架构,导致性能不佳.
研究的目的:
- 通过自动化模型架构设计来解决EHR数据中的多模式挑战.
- 提出一个新的神经架构搜索 (NAS) 框架,AutoFM,以实现最佳的EHR数据编码和融合.
- 提高深度学习模型在挖掘复杂EHR数据中的性能.
主要方法:
- 开发了AutoFM,一个神经架构搜索 (NAS) 框架.
- 该框架自动搜索各种输入方式和融合策略的最佳架构.
- 对现实世界多式联络电子记录数据进行实验,用于预测任务.
主要成果:
- 与最先进的方法相比,AutoFM实现了显著的性能改进.
- 该框架有效地发现了有意义和优化的网络架构.
- 在挖掘多模式EHR数据方面表现出卓越的性能.
结论:
- 使用AutoFM等NAS框架的自动化模型设计对EHR数据有效.
- 在EHR分析中,AutoFM提供了一个强大的解决方案来克服多模式的挑战.
- 拟议的框架有可能促进医疗保健中的深度学习应用.
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