# MSA K-BERT: A Method for Medical Text Intent Classification
## 6 Discussion  
As one of the core tasks of NLP in smart healthcare [62], medical text intent classification is critical to promoting the accuracy of the diagnostic process, building decision support systems (CDSS) [63], and optimizing doctor-patient interaction. However, existing models still suffer from an insufficient understanding of specialized medical terminology and contextual semantics, as well as ineffective integration of medical KG or domain-specific prior knowledge in practical applications, which weakens semantic support for intent recognition and results in low classification accuracy. Therefore, integrating medical domain knowledge to improve semantic comprehension as well as enhancing the model's generalization ability are problems that need to be resolved in the current research on the intent classification of medical texts.  
In this study, we propose a knowledge-enhanced bidirectional encoder representation model that combines multi-scale attention mechanisms. Compared to traditional BERT-based text classification models, MSA K-BERT introduces a BERT-compatible knowledge-supported LR framework that enhances the textual representation with fine-grained medical KG injection strategies, enabling the model to more efficiently capture the implicit or ambiguous semantics commonly found in medical texts. In addition, the introduction of the MSA strengthens the features of different representation layers of our model [60], which significantly promotes the classification accuracy and interpretability of our model. It is worth noting that in our experiments on the IMCS-21 dataset, the accuracy of MSA K-BERT is 0.826, the recall rate is 0.794, and the F1 score is 0.810. The method we propose achieves superior performance and significantly outperforms both conventional and deep learning-based methods. These results verify the effectiveness of MSA K-BERT in identifying the intent categories of complex medical texts and demonstrate its potential in smart healthcare applications.  
However, the computational overhead brought by knowledge injection and multi-scale concern mechanisms may limit the scalability of the model in resource-limited or real-time environments. Noisy or incomplete knowledge may introduce irrelevant information, potentially reducing the model's performance in specific medical scenarios. Another limitation of this study is the absence of direct experimental comparison with large-scale general-purpose language models such as GPT and LLaMA. These models have demonstrated impressive capabilities on a wide range of NLP tasks. However, due to their considerable computational demands, lack of fine-tuned variants for Chinese medical text, and potential data privacy risks, we focus our work on a lightweight and controllable BERT-based framework that is more suitable for domain-specific applications in healthcare. In future work, we plan to explore the integration and comparison of MSA K-BERT with multilingual or general-purpose LLMs under both zero-shot and fine-tuned settings to further understand their performance trade-offs in specialized domains.  
Furthermore, we will explore dynamic knowledge selection strategies to further mitigate KN and investigate reducing computational complexity while maintaining performance. Another important future work we are interested in is to explore the extension of the model to support cross-lingual and multi-modal medical data processing, thus  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Baseline (KBERT)** & **Attention** & **MSA** & **Accuracy** & **Recall** & \(\mathbf{F_{1}}\) \\ \hline \(\bigstar\)1 & & & 0.807 & 0.727 & 0.736 \\ \(\bigstar\) & \(\bigstar\) & & 0.816 & 0.719 & 0.764 \\ \(\bigstar\) & \(\bigstar\) & \(\bigstar\) & 0.826 & 0.794 & 0.810 \\ \hline \hline \end{tabular} \({}^{\dagger}\) indicates the model components activated in this experimental configuration.  
\end{table}
Table 6: The results of the ablation experiments.  
enhancing its robustness and adaptability in different clinical environments to realize superior performance.