# Named Entity Recognition in Online Medical Consultation Using Deep Learning
## 4 Research Design and Analysis of Results
### Experimental Results and Discussion  
#### 4.4.1 Evaluative Comparison Across Methods  
As illustrated by the first nine rows in Table 7, to ensure a balanced comparison with previous research, in this study, we established nine baseline methods based on the literature to validate the efficacy of the proposed approach. As Rows 10 and 11 of Table 7 show, to evaluate the performance of the SOTA LLM on the constructed dataset in this study, we established two baseline methods based on zero-shot prompting: ChatGPT-4o_26_tags and ChatGPT-4o_5_tags. ChatGPT-4o_26_tags represents the overall performance of ChatGPT-4o ([[https://chatgpt.com/](https://chatgpt.com/)]([https://chatgpt.com/](https://chatgpt.com/)), accessed on 3 February 2025) across all 26 entity categories in the constructed dataset, while ChatGPT-4o_5_tags reflects its overall performance on the five common entity categories (i.e., body, symptom, check, disease, and treatment) consistent with the categories used in the literature [35]. As Row 12 of Table 7 shows, to prove the novelty of the approach proposed in this study, the latest SOTA method [25] was also introduced for comparison. Notably, for a fair comparison, we replaced the BERT models in all baseline methods with CBERT, which demonstrates a superior performance and is consistent with the proposed approach in this study. From Table 7, the following conclusions can be drawn:  
* According to Rows 1-3 in Table 7, the F1 scores of CBERT, CBERT-CRF, and CBERT-MCRF were 80.33%, 82.10%, and 83.42%, respectively. The experimental results show that the model performance can be effectively improved by adding the decoding layer constraint on the CBERT basis. Better performance is obtained because MCRF provides a stronger constraint relationship than CRF and effectively solves the problem of CRF occasionally generating illegal tag sequences. * According to Rows 3, 5, and 7 in Table 7, the F1 scores of CBERT-MCRF, CBERT-SCNN-MCRF, and CBERT-BiLSTM-MCRF were 83.42%, 84.06%, and 84.37%, respectively. The data indicate that both the SCNN and BiLSTM neural networks can enhance the model's capability to learn features, with BiLSTM demonstrating a more robust feature learning capacity compared to SCNN. This is because SCNN is more suitable for spatial feature extraction scenarios (such as video processing and image \begin{table}
\begin{tabular}{l l} \hline
**Parameter Name** & **Value** \\ \hline Max sequence length & 256 \\ Learning rate & \(2\times 10^{-05}\) \\ Optimizer & Adam \\ Adam epsilon & \(1\times 10^{-08}\) \\ Hidden dropout ratio & 0.2 \\ Warmup proportion & 0.02 \\ Weight decay & 0.01 \\ Max epochs & 20 \\ Batch size & 1 \\ BiLSTM size & 384 \\ \hline \end{tabular}
\end{table} 
Table 6: Parameter settings of the proposed approach. segmentation), while BiLSTM is more suitable for processing sequential data (such as text). * As Rows 5, 7, and 8 in Table 7 show, the F1 scores of CBERT-SCNN-MCRF, CBERT-BiLSTM-MCRF, and CBERT-SCNN-BiLSTM-MCRF were 84.06%, 84.37%, and 85.17%, respectively. The data indicate that the combination of SCNN and BiLSTM in parallel can complement the advantages of the two methods, which can fully utilize the advantage of SCNN in local feature extraction and fully exploit the advantage of BiLSTM in global feature extraction, thus improving the model's performance further. * As Rows 8 and 9 in Table 7 show, the F1 scores of CBERT-SCNN-BiLSTM-MCRF and CBERT-SCNN-Local_Context-BiLSTM-Global_Context-MCRF were 85.17% and 85.24%, respectively. The experimental results show that adding the local context mechanism module after SCNN and the global context mechanism module after BiLSTM can notably enhance model performance, thereby confirming the efficacy of the local context mechanism module in learning local contextual semantic representation and the global context mechanism module in learning global contextual semantic representation.