Enhanced effective convolutional attention network with squeeze-and-excitation inception module for multi-label clinical document classification.

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.