## 4 Experiment
### Experiment on BIBC
#### 4.3.2 Results and Analysis  
The results of our ablation studies performed on the diabetes dataset are shown in the following Table 3.  
From the result, we can discover that single models are generally less effective than composite models. Among the three single model, deep learning based methods have better results than statistical methods, namely CRF. In addition, BiLSTM achieves the best performance in single models which means that bidirectional context information does improve the effectiveness. As is shown in Table 3, BiLSTM-CRF, a mainstream model, works better than single models. This is because each layer of the composite model focused on different features. For example, BiLSTM-CRF compared with Bi-LSTM has an additional CRF layer, the main purpose of which was to learn the constraints of the sentence and reduce the sequence of incorrect predictions. Meanwhile, the introduction of BERT increases the score by 2.53%. This comparison between BiLSTM-CRF and BERT-BiLSTM-CRF indicates that the word vectors obtained from BERT are relatively better than vectors obtained from Word2 vec, and the problem of polysemy is alleviated. Thus, the \begin{table}
\begin{tabular}{c c} \hline \hline
**Parm Type** & **Parm Value** \\ \hline Max seq length & 256 \\ Batch size & 64 \\ Learning rate & \(5\times 10^{-6}\) \\ Epoch & 20 \\ Dropout & 0.5 \\ Clip & 5 \\ filter & 100 \\ Bi-LSTM Hidden Layer Size & 128 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model parameter configuration. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline CRF & 68.31 & 66.93 & 67.61 \\ LSTM & 70.82 & 72.32 & 71.56 \\ BiLSTM & 72.35 & 73.09 & 72.72 \\ BiLSTM-CRF & 73.41 & 75.93 & 74.65 \\ IDCNN-CRF & 72.95 & 73.47 & 73.21 \\ BERT-CRF & 76.53 & 77.85 & 77.18 \\ BERT-BiLSTM-CRF & 78.35 & 78.15 & 78.25 \\ BERT(wwm)-BiLSTM-CRF & 79.01 & 78.83 & 78.92 \\ Our model & 79.58 & 80.21 & 79.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of entity recognition results. pre-trained model has already extracted the linguistic knowledge and encoded it into the network, which can introduce a large amount of external knowledge from unsupervised data compared to the other two methods. The result of IDCNN-CRF surpasses that of BiLSTM-CRF, while BiLSTM is capable of extracting more distant dependency information and CNN focuses more on local information. It shows that the effect of using convolutional neural network alone is not as good as using bidirectional LSTM. The adjustment of the masking method in the modified BERT(BERT-WWM) makes the model work better than BERT-base, which means, in Chinese NLP tasks, that the whole word masking setting can effectively improve the model performance. The improvement achieved in our work could benefit the research on diabetes and other related medical fields. The accuracy, recall, and F1 values of the proposed BIBC model are improved by 0.57%, 1.38%, and 0.97%, respectively, compared with BERT(wwm)-BiLSTM-CRF. This experimental result indicates that BERT combined with IDCNN can obtain more local information, BiLSTM can obtain long-range contextual information, and the final feature vector contains a large amount of semantic information of the text, which improves the effect of the final NER task. In summary, the BIBC-based named entity recognition model works better than other aforementioned statistical or deep learning models, and the effectiveness of the model is verified through our experiments. In the phrase \"A blood glucose level below 3.9 mmol/L (70 mg/d1) in people with diabetes is known as hypoglycaemia\", the BIBC identifies \"hypoglycaemia\" as a Disease, but it is actually classified as a Symptom. The reason for this may be that there is some ambiguity in the entity and the presence of \"is known as\" makes it appear that the entity \"hypoglycaemia\" can also be classified as a Disease. Another reason is that the entity of \"hypoglycemia\" belongs to multiple categories.