## 4 Empirical Studies
### Classification of Short Medical Texts in Different Sections  
After the training is completed, Python code is used to plot the direction of the loss of the training set and the validation set during the training of the model, as shown in Figure 11. From the figure, it can be seen that around the 10 th round of training, the loss value starts to decrease very slowly, until the 26 th round, when the loss value of the validation set starts to increase slightly, and the training of the model is completed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy** & **Weighted Avg F1** \\ \hline FCNN & 0.9210 & 0.9208 \\ DNN & 0.9318 & 0.9315 \\ BERT & 0.9620 & 0.9608 \\ BERT-RNN & 0.9632 & 0.9624 \\ BERT-RCNN & 0.9668 & 0.9664 \\ Prompt-BERT-RCNN & 0.9784 & 0.9782 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of classification effects of classifiers after short text feature expansion.  
In order to verify the classification effect of the extended Prompt-BERT-RCNN model on each topic category, the text calculated the weighted average F1 values of short medical texts for each subject category, and the results are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Administrative Division** & **Precision** & **Recall** & **Weighted** \\  & & & **Avg F1** \\ \hline TCM & 0.970 & 0.961 & 0.9654 \\ Clinical Immunology & 0.980 & 0.973 & 0.9765 \\ Interventional Department & 0.958 & 0.951 & 0.9543 \\ Peds & 0.976 & 0.969 & 0.9723 \\ Ortho Joint Surg & 0.968 & 0.959 & 0.9634 \\ Endo & 0.970 & 0.961 & 0.9654 \\ Bone Tumor \& Bone Disease Surgery & 0.956 & 0.949 & 0.9523 \\ Heme & 0.965 & 0.957 & 0.9610 \\ GI Surg & 0.961 & 0.952 & 0.9565 \\ Oncology & 0.959 & 0.950 & 0.9545 \\ Nephrology & 0.958 & 0.949 & 0.9534 \\ Hepatobiliary Surg & 0.978 & 0.971 & 0.9745 \\ ENT \& Head Neck Surg & 0.967 & 0.958 & 0.9623 \\ Neurosurgery & 0.962 & 0.952 & 0.9567 \\ Neurology & 0.964 & 0.954 & 0.9587 \\ Ophthalno & 0.975 & 0.968 & 0.9712 \\ Derm & 0.975 & 0.966 & 0.9703 \\ Path & 0.966 & 0.957 & 0.9612 \\ Breast, Thyroid \& Vasc Surg & 0.978 & 0.971 & 0.9745 \\ Burns \& Skin Surg & 0.968 & 0.959 & 0.9634 \\ GI & 0.969 & 0.960 & 0.9645 \\ Nuclear Med & 0.960 & 0.952 & 0.9556 \\ Rad/Once & 0.972 & 0.964 & 0.9676 \\ Plast Surg & 0.964 & 0.955 & 0.9590 \\ Hand Surg & 0.971 & 0.962 & 0.9665 \\ Infectious Disease Ctr & 0.962 & 0.952 & 0.9565 \\ Psychosomatic Med & 0.971 & 0.961 & 0.9656 \\ Cardiology & 0.997 & 0.992 & 0.9944 \\ Cardiothoracic Surg & 0.955 & 0.946 & 0.9501 \\ Pulm Med & 0.987 & 0.979 & 0.9828 \\ OB/GYN & 0.989 & 0.981 & 0.9846 \\ Ortho & 0.987 & 0.979 & 0.9828 \\ Spine Surg & 0.966 & 0.956 & 0.9609 \\ Urology & 0.982 & 0.973 & 0.9771 \\ Reproductive Ctr & 0.997 & 0.991 & 0.9936 \\ Pain Mgmt & 0.992 & 0.983 & 0.9872 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Values of each evaluation indicator of the model.  
Figure 11: Trend of model training loss rate.  
The data in Table 6 show that the weighted average F1 scores for the different medical topic categories are generally high, indicating that the classification method we used achieves satisfactory results in the medical Q&A domain as a whole.  
Overall, the Prompt-BERT-RCNN model combined with keyword expansion proposed in this paper performs well overall in categorizing departments. In particular, in the categories of cardiology and respiratory medicine, the model demonstrated a very high weighted average F1 score. However, there is still room for improvement in the classification of other internal medicine categories and cardiac macrovascular surgery, mainly because the cardiac macrovascular surgery category involves a greater diversity of physiological mechanisms and pathological states, whereas the other internal medicine categories have a wider coverage of quiz content and less distinctive features than the other specialties, which results in the model facing more challenges in feature capturing and information processing.