Venkata Krishna Reddy M, Raghavendar Raju L, Sai Prasad Kashi, Kumari Dr D Anitha, Veerabhadram Vadlamani, Yamsani Nagendar
Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology (Autonomous), Gandipet, Hyderabad, India.
Department of Computer Science and Engineering, Matrusri Engineering College, Hyderabad, India.
Sci Rep. 2025 May 16;15(1):16988. doi: 10.1038/s41598-025-98719-0.
Clinical Document Classification (CDC) is crucial in healthcare for organizing and categorizing large volumes of medical information, leading to improved patient care, streamlined research, and enhanced administrative efficiency. With the advancement of artificial intelligence, automatic CDC is now achievable through deep learning techniques. While existing research has shown promising results, more effective and accurate classification of long clinical documents is still desired. To address this, we propose a new model called the Enhanced Effective Convolutional Attention Network (EECAN), which incorporates a Squeeze-and-Excitation (SE) Inception module to improve feature representation by adaptively recalibrating channel-wise feature responses. This architecture introduces an Encoder and Attention-Based Clinical Document Classification (EAB-CDC) strategy, which utilizes sum-pooling and multi-layer attention mechanisms to extract salient features from clinical document representations. This study proposes EECAN (Enhanced Effective Convolutional Attention Network) as the overall model architecture and EAB-CDC (Encoder and Attention-Based Clinical Document Classification) as a core strategy conducted in EECAN. EAB-CDC is not a standalone model but a functional part applied to the architecture for discriminative feature extraction by sum-pooling and multi-layer attention mechanisms. With this integrated design, EECAN can transform multi-label clinical texts' general and label-specific contexts without losing information. Our empirical study, conducted on benchmark datasets such as MIMIC-III and MIMIC-III-50, demonstrates that the proposed EECAN model outperforms several existing deep learning approaches, achieving AUC scores of 99.70% and 99.80% using sum-pooling and multi-layer attention, respectively. These results highlight the model's substantial potential for integration into clinical systems, such as Electronic Health Record (EHR) platforms, for the automated classification of clinical texts and improved healthcare decision-making support.
临床文档分类（CDC）在医疗保健领域对于组织和分类大量医疗信息至关重要，有助于改善患者护理、简化研究并提高行政效率。随着人工智能的发展，现在可以通过深度学习技术实现自动CDC。虽然现有研究已显示出有前景的结果，但仍需要对长临床文档进行更有效和准确的分类。为了解决这个问题，我们提出了一种名为增强有效卷积注意力网络（EECAN）的新模型，该模型结合了挤压激励（SE）Inception模块，通过自适应地重新校准通道级特征响应来改善特征表示。这种架构引入了基于编码器和注意力的临床文档分类（EAB-CDC）策略，该策略利用求和池化和多层注意力机制从临床文档表示中提取显著特征。本研究提出EECAN（增强有效卷积注意力网络）作为整体模型架构，EAB-CDC（基于编码器和注意力的临床文档分类）作为在EECAN中实施的核心策略。EAB-CDC不是一个独立的模型，而是应用于该架构的一个功能部分，用于通过求和池化和多层注意力机制进行判别性特征提取。通过这种集成设计，EECAN可以转换多标签临床文本的一般和特定标签上下文而不丢失信息。我们在MIMIC-III和MIMIC-III-50等基准数据集上进行的实证研究表明，所提出的EECAN模型优于几种现有的深度学习方法，分别使用求和池化和多层注意力时，AUC分数达到99.70%和99.80%。这些结果突出了该模型在集成到临床系统（如电子健康记录（EHR）平台）中用于临床文本自动分类和改善医疗决策支持方面的巨大潜力。
