增强有效的卷积注意网络与挤压和激发启动模块,用于多标签临床文档分类.
M Venkata Krishna Reddy1, L Raghavendar Raju2, Kashi Sai Prasad3
1Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology (Autonomous), Gandipet, Hyderabad, India. krishnareddy_cse@cbit.ac.in.
Scientific reports
|May 16, 2025
概括
增强有效卷积注意网络 (EECAN) 使用深度学习改进了临床文档分类. 这种人工智能模型增强了功能提取,以更准确地组织电子健康记录中的医疗信息.
科学领域:
- 医疗保健中的人工智能
- 医学信息学的深度学习
- 临床环境中的自然语言处理.
背景情况:
- 临床文档分类 (CDC) 对于管理大量的医疗数据,改善患者护理,研究和行政任务至关重要.
- 目前的深度学习模型显示出有希望的结果,但对于长长的临床文档来说,需要提高准确性.
- 越来越需要先进的AI解决方案来有效地分类复杂的医学文本.
研究的目的:
- 引入增强有效卷积注意网络 (EECAN) 以改进临床文档的自动分类.
- 通过使用新型深度学习策略,增强功能表示和从临床文档提取.
- 解决目前用于分类冗长和多标签临床文本的方法的局限性.
主要方法:
- 提出了EECAN模型,集成了一个Squeeze-and-Excitation (SE) Inception模块,用于适应性特征重新校准.
- 在EECAN中引入了编码器和基于注意力的临床文档分类 (EAB-CDC) 策略.
- 利用总和聚合和多层注意力机制从临床文本表示中提取歧视性特征.
主要成果:
- 与基准数据集 (MIMIC-III,MIMIC-III-50) 上现有的深度学习方法相比,EECAN表现出优异的性能.
- 获得了高AUC分数的99.70%和总和和99.80%的多层注意力.
- 该模型有效地改变了多标签临床文本的上下文,而不会丢失信息.
结论:
- 该EECAN模型在自动化临床文档分类方面取得了重大进展.
- 它的高准确性和效率表明,它有很大的潜力可以集成到电子健康记录 (EHR) 系统中.
- 这种方法可以通过改善医疗信息组织来加强医疗保健决策支持.
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