摘要：

在医疗健康领域中, 现有的问题分类方法存在文本特征表示能力弱的问题, 并且对于多类别问题, 忽视了不同关键词特征的权重, 从而影响了分类的准确性。为了解决这些问题, 提出一种基于多特征融合与混合神经网络的医疗健康问题分类方法(MPC-MFF-HNN), 旨在提高医疗健康问题分类的准确性。首先, 该方法结合RoBERTa-wwm-ex模型和Word2Vec模型对文本信息进行字符级和单词级的向量表示, 以获得丰富的多特征信息, 从而弥补单一特征表示方法的不足, 使得模型在处理复杂的医疗健康文本时能够更全面地理解和表征文本语义; 其次, 通过多头注意力机制结合改进的文本卷积神经网络(TextCNN)和双向门控循环单元(BiGRU), 设计了一种混合神经网络模型MHA-APTC-BiGRU, 其采用多层次特征提取方法, 能够有效提取包含关键词权重的深层次文本特征; 最后, 分类器将语义增强的特征向量作为输入, 用于问题类别的分类。在真实公开数据集上的实验结果表明, 与其他基线算法相比, 该方法在精确率、召回率和F1值指标上均显著提升, 在医疗健康问题分类方面表现出更优越的性能。

关键词: 多特征融合, 混合神经网络, 多标签文本分类, 注意力机制, 医疗健康

Abstract:

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.

Key words: multi-feature fusion, hybrid neural network, multi-label text classification, attention mechanism, medical health