# Enhanced Precision in Chinese Medical Text Mining Using the ALBERT+Bi-LSTM+CRF Model
## 4 Experimental Process
### Results and Analysis  
#### 4.3.1 Results  
Experiments were conducted in two distinct forms. However, the primary focus was on extracting entities. Table 3 presents a detailed analysis of the experimental results. To demonstrate the effectiveness of the proposed algorithm, well-known algorithms were used in comparison based on the CCKS2020 dataset [29, 30]. We observed that the ALBERT+Bi-LSTM+CRF algorithm achieved satisfactory results in the F1-score, accuracy, and recall metrics. Notably, when compared with the BERT+Bi-LSTM+CRF algorithm, it significantly reduced the number of model parameters and the algorithm's complexity, yet still maintained a high performance level. We also analyzed the impact of incorporating the Bi-LSTM+CRF module on the model's performance, and we found that the ALBERT algorithm could swiftly converge and attain stable outcomes in a short training cycle. However, the addition of more training cycles did not bring improved outcomes. Since strict requirements for accurate data apply in the medical field, BiLSTM and CRF were introduced to construct a more refined algorithm. As shown in Figure 4, although the proposed algorithm performed relatively poorly in the early stages of training, it succeeded in producing a better outcome within an acceptable time frame if the training cycle was appropriately extended. Specifically, the F1-score, precision, and recall of the ALBERT+BILSTM+CRF algorithm reached 91.8%, 92.5%, and 94.3%, respectively. When \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Relation Type**} & \multicolumn{2}{c}{**Train Set**} & \multicolumn{2}{c}{**Validation Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{2-7}  & **Entries** & **Percentage/\%** & **Entries** & **Percentage/\%** & **Entries** & **Percentage/\%** \\ \hline Epidemiological & 3895 & 4.5 & 1002 & 4.6 & 1043 & 4.8 \\ Prognosis & 388 & 0.1 & 110 & 0.5 & 107 & 0.5 \\ Sociological & 6649 & 7.6 & 1720 & 7.9 & 1756 & 8.1 \\ Synonyms & 4848 & 5.6 & 1144 & 5.2 & 1084 & 5.0 \\ Prevention & 2479 & 2.9 & 540 & 2.5 & 567 & 2.6 \\ Post-treatment & 147 & 0.1 & 37 & 0.1 & 55 & 0.3 \\ Imaging & 2238 & 2.6 & 505 & 2.3 & 521 & 2.4 \\ Medication & 6690 & _7.7_ & 1547 & 7.1 & 1488 & 6.9 \\ Symptom & 22,373 & 25.7 & 5678 & 26.0 & 5621 & 25.9 \\ Pathological & 4304 & 4.9 & 1182 & 5.4 & 1145 & 5.3 \\ staging & 1216 & 1.4 & 318 & 1.5 & 309 & 1.4 \\ Complications & 5187 & 6.0 & 1324 & 6.1 & 1297 & 6.0 \\ Other & 1576 & 1.8 & 394 & 1.8 & 408 & 1.9 \\ inspections & 1681 & 1.9 & 404 & 1.8 & 391 & 1.8 \\ Diagnosis & 2900 & 3.3 & 754 & 3.4 & 776 & 3.6 \\ Body part & 4376 & 5.0 & 1130 & 5.2 & 1106 & 5.3 \\ Other & 2848 & 3.3 & 715 & 3.3 & 703 & 3.2 \\ treatments & 3210 & 3.7 & 851 & 3.9 & 844 & 3.9 \\ Correlation & 9971 & 11.5 & 2529 & 11.6 & 2487 & 11.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The distribution of the relation extractions in the datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **F1-Score/\%** & **Precision/\%** & **Recall/\%** \\ \hline Word2 vec-BiLSTM-CRF & 75.1 & 73.2 & 72.8 \\ Bi-LSTM+CRF & 88.6 & 88.2 & 88.4 \\ BERT+Bi-LSTM+CRF & 90.1 & 94.9 & 92.3 \\ ALBERT & 90.5 & 87.2 & 89.6 \\ ALBERT+Bi-LSTM+CRF & 91.8 & 92.5 & 94.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results for entity extraction. ALBERT was compared with the proposed algorithm, the F1-score, precision, and recall increased by 1.3%, 5.3%, and 4.7%, respectively. This outcome indicates that key entities can be effectively extracted from medical texts. Thus, robust support for the construction of medical knowledge graphs is provided. Also, clinical implementations can be combined with advanced deep learning architectures and sequence labeling techniques. Table 4 presents the experimentation's results in relation extraction. Once the algorithm was constructed, the triplets from the text were attained by using the constructed entity extraction and relationship extraction algorithms.