# Named Entity Recognition in Online Medical Consultation Using Deep Learning
## 4 Research Design and Analysis of Results
### Experimental Results and Discussion
#### 4.4.2 Ablation Study  
As Table 8 shows, ablation experiments were conducted using the experimental dataset. From Table 8, the following conclusions can be drawn:  
* As Rows 1 and 2 in Table 8 show, after the approach proposed in this study removed the deep factorization machine with the fusion context mechanism component DeepFM-Fusion_Context, the model's F1 score dropped by 0.23%, indicating that this component is effective in fusing the local and global context semantic representations. * As Rows 1 and 3 of Table 8 show, after removing the deep factorization machine with the fusion context mechanism component DeepFM-Fusion_Context from the approach proposed in this study, the local context mechanism module Local_Context and the global context mechanism module Global_Context were further removed and the model's F1 score dropped by 0.30%, indicating the effectiveness of the Local_Context module and the Global_Context module in improving model performance. * As Rows 1 and 4 in Table 8 show, after removing the deep factorization machine with the fusion context mechanism component DeepFM-Fusion_Context, the local context mechanism module Local_Context, and the global context mechanism module Global_Context from the approach proposed in this study, on this basis, further removing the stacked CNN (SCNN) module decreased the F1 score of the model by 1.10%, indicating that the local semantic features extracted by this module are important in improving model performance. * As Rows 1 and 5 of Table 8 show, after removing the deep factorization machine with the fusion context mechanism component DeepFM-Fusion_Context, the local context mechanism module Local_Context, and the global context mechanism module Global_Context from the approach proposed in this study, on this basis, the BiLSTM module was further removed and then the model's F1 score dropped by 1.41%, \begin{table}
\begin{tabular}{c l c c c} \hline \hline \# & **Model** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline
1 & CBERT [36] & \(77.54\pm 1.49\) & \(83.34\pm 0.98\) & \(80.33\pm 1.24\) \\
2 & CBERT-CRF [36; 37] & \(79.54\pm 0.97\) & \(84.83\pm 0.47\) & \(82.10\pm 0.61\) \\
3 & CBERT-MCRF & \(83.84\pm 0.52\) & \(83.01\pm 1.11\) & \(83.42\pm 0.81\) \\
4 & CBERT-SCNN-CRF & \(80.15\pm 2.29\) & \(84.47\pm 0.83\) & \(82.24\pm 1.57\) \\
5 & CBERT-SCNN-MCRF & \(84.65\pm 0.41\) & \(83.47\pm 0.65\) & \(84.06\pm 0.45\) \\
6 & CBERT-BiLSTM-CRF [21; 37; 38] & \(82.33\pm 0.66\) & \(85.73\pm 0.48\) & \(84.00\pm 0.55\) \\
7 & CBERT-BiLSTM-MCRF & \(84.38\pm 0.59\) & \(84.36\pm 0.46\) & \(84.37\pm 0.31\) \\
8 & CBERT-SCNN-BiLSTM-MCRF & \(85.10\pm 0.87\) & \(85.25\pm 0.94\) & \(85.17\pm 0.78\) \\
9 & CBERT-SCNN-Local_Context-BiLSTM- & \(84.73\pm 0.33\) & \(\mathbf{85.77\pm 0.69}\) & \(85.24\pm 0.30\) \\
10 & ChatGPT-4o\_26\_tags (accessed on 3 February 2025) & \(55.01\pm 0.00\) & \(34.15\pm 0.00\) & \(42.14\pm 0.00\) \\
11 & ChatGPT-4o\_5\_tags (accessed on 3 February 2025) & \(57.97\pm 0.00\) & \(51.04\pm 0.00\) & \(54.29\pm 0.00\) \\
12 & CBERT-BiLSTM-context (SOTA, [25]) & \(84.52\pm 0.77\) & \(84.44\pm 0.69\) & \(84.48\pm 0.59\) \\  & CBERT-SCNN-Local_Context-BiLSTM- & \(\mathbf{85.56\pm 0.73}\) & \(85.38\pm 0.59\) & \(\mathbf{85.47\pm 0.56}\) \\
13 & Global\_Context-DeepFM-Fusion\_Context-MCRF & \(\mathbf{85.56\pm 0.73}\) & \(85.38\pm 0.59\) & \(\mathbf{85.47\pm 0.56}\) \\  & (ours) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of our approach against baseline models. Here, P, R, and F1 denote precision, recall, and F1 scores, respectively, which are three performance evaluation metrics for models. Optimal results are highlighted in **bold**. CBERT denotes the ChineseBERT pretrained model, SCNN denotes the stacked convolutional neural network, and MCRF represents the masked conditional random field. indicating that the global semantic features extracted by this module are critical for improving model performance.