## 5 Discussion  
In this study, we proposed a Chinese medical named entity recognition (NER) model that combines the BERT pre-trained model, RNNs, attention mechanism, novel memory units (GLMU and RMN), and fully connected layers. To investigate whether the enhanced performance of the BERT-GLMU+RMN-FC-CRF method in entity recognition is primarily due to the novel memory unit and fully connected layer, we conducted the following ablation experiments:  
1. In the first ablation experiment, we removed the fully connected layer, retaining only the BERT-GLMU+RMN model with the CRF layer, to compare the performance of the model without the fully connected layer to that of the full model. 2. In the second ablation experiment, we retained the BERT-FC-CRF model and removed the novel memory unit (GLMU+RMN) to evaluate the contribution of the novel memory unit to overall model performance. 3. In the third ablation experiment, we retained the BERT-CRF model and removed both the novel memory unit (GLMU+RMN) and the fully connected layer (FC) to evaluate their contributions to the overall model performance. The results of the ablation experiments are shown in Figure 2, where the BERT-GLMU+RMN-FC-CRF model slightly outperforms the BERT-GLMU+RMN-CRF, BERT-FC-CRF, and BERT-CRF models across all three metrics. These results suggest that the improvement in model effectiveness relies on the proposed dependency perception and novel memory unit modules. This indicates that the novel memory unit (GLMU+RMN) and the fully connected layer (FC) in our model positively impact performance in medical \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Set** & **Model** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{MCSCSet} & BERT-base-crf & 90.22 & 87.41 & 88.79 \\  & BERT-wwm-crf & 90.82 & 88.20 & 89.49 \\ \cline{1-1}  & MacBERT-base-crf & 89.13 & 87.98 & 88.55 \\ \cline{1-1}  & RoBERTa-crf & 92.10 & 90.52 & 91.30 \\ \cline{1-1}  & BERT-GLMU+RMN-FC-CRF (Ours) & 92.28 & 90.61 & 91.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of the MCSCSet dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Model** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{CMeEE} & MacBERT-large [28] & - & - & 62.40 \\  & BERT-Biaffine [29] & 64.17 & 61.29 & 62.29 \\ \cline{1-1}  & Span-level NER [30] & 66.40 & 62.40 & 64.50 \\ \cline{1-1}  & PSA [31] & 65.97 & 60.96 & 63.36 \\ \cline{1-1}  & BERT-GLMU+RMN-FC-CRF (Ours) & 66.24 & 64.93 & 64.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of the CMeEE dataset. named entity recognition tasks. The experimental results show that the model outperforms existing related models on Chinese medical datasets, demonstrating significant advantages in handling complex named entity structures and identifying domain-specific terms. As shown in Figure 2, the model performs robustly in terms of precision even after the removal of the fully connected layer (FC), with a precision rate of 92.23%, compared to the original model BERT-GLMU+RMN-FC-CRF's 92.28%. However, its recall decreases slightly to 90.42% compared to the original model's 90.61%. This suggests that the absence of the fully connected layer reduces the model's ability to detect entities. In terms of F1 score, BERT-GLMU+RMN-CRF achieves 91.26%, slightly lower than the 91.53% of the original model. This indicates that the model without the fully connected layer may not handle complex tasks, particularly entity detection, as effectively. When the novel memory unit (GLMU+RMN) is removed, the model's precision drops to 92.23% and recall decreases to 89.36% compared to the original model's 92.28% precision and 90.61% recall. This suggests that removing the novel memory unit significantly reduces the model's ability to detect entities. In terms of F1 score, BERT-FC-CRF achieves 89.65%, which is lower than the original model's 91.53%.