Medical and Health Question Classification Based on Multi-feature Fusion and Hybrid Neural Network

In the field of healthcare, existing methods for problem classification suffer from weak text feature representation and often overlook the varying weights of different keywords in multi-class scenarios, thereby affecting classification accuracy. To address these issues, a Medical Problem Classification method based on Multi-Feature Fusion and a Hybrid Neural Network (MPC-MFF-HNN) is proposed. This method aims to enhance the accuracy of the healthcare problem classification. First, the approach combines the RoBERTa-wwm-ext and Word2Vec models to represent text information at both the character and word levels, thus obtaining rich multi-feature information. This approach compensates for the limitations of single-feature representation methods and enables the model to comprehensively understand and characterize complex healthcare texts. Second, a hybrid neural network model named MHA-APTC-BiGRU is designed, incorporating multi-head attention mechanisms with an enhanced Text Convolutional Neural Network (TextCNN) and a Bidirectional Gated Recurrent Unit (BiGRU). This model uses multi-level feature extraction methods to effectively capture deep-level text features, including keyword weights. Finally, the classifier uses these semantically enhanced feature vectors for problem category classification. Experiments on real-world public datasets reveal significant improvements in precision, recall rate, and F1 score metrics compared with other baseline algorithms, demonstrating superior performance in healthcare problem classification.