## 2 Materials and Methods
### MGFGF-BiLSTM-EGP
Similarly, the CMeEE V2 dataset initially showed poor category aggregation, with blurred boundaries and significant overlap under the original word vectors. The application of MFGFF significantly clarified these boundaries and improved data point aggregation, highlighting its effectiveness in enhancing feature differentiation. However, certain categories such as medical examination item, medical procedure, and body still exhibited similarities, likely due to semantic overlaps in medical terminology. In contrast, the CLUENER dataset, a general domain corpus, displayed a more even distribution of entities under the original word vectors, though noticeable category overlap was still present. The application of MFGFF greatly improved differentiation between entity types, which may be attributed to the dataset's inherent diversity in entity text, allowing fused features to better segregate categories. In summary, our analysis highlights the complexities inherent in biomedical NER including challenges like semantic overlap, domain-specific vocabulary, and nested entities. While MFGFF demonstrated significant advantages in enhancing feature representation, it still faced challenges, particularly in addressing category similarity. The varying performance of this technique across different datasets is closely tied to the characteristics of the datasets and the semantic features of the entity categories, indicating a need for further optimization and adaptation in specific applications. ### Nested Entity Predictive Analytic

Based on the experimental results presented in Figure 10, we conducted a detailed evaluation of the effectiveness of nested entity recognition by comparing the performance of fine-tuning (FT) RoBERTa and MFGFF across different datasets. For the CDNER dataset, fine-tuning RoBERTa achieved a precision of 79.68%, a recall of 72.54%, and an F1 score of 75.98%. On the CMeEE V2 dataset, these metrics were notably lower, with a precision of 42.13%, a recall of 47.15%, and an F1 score of 44.50%. Compared to the overall entity recognition, the F1 scores for CDNER and CMeEE V2 decreased by 16.03% and 28.82%, respectively, highlighting the significant challenges posed by nested entities in NER. To address this challenge, we developed a high-quality nested entity dataset to enhance model training with more representative data. Implementing the MFGFF method further improved the model's capability to recognize nested entities. On the CDNER dataset, MFGFF achieved precision, recall, and F1 scores of 71.90%, 72.13%, and 71.91%,

Figure 10: P, R, and F1 evaluation results of the fine-tuning method and MFGFF method on the CDNER and CMeEE V2 datasets. respectively, closely aligning with the performance of fine-tuning RoBERTa. On the more complex CMeEE V2 dataset, MFGFF yielded precision, recall, and F1 scores of 39.56%, 41.10%, and 40.32%, respectively. Although MFGFF's absolute values were slightly lower than those of the fine-tuned RoBERTa, its stability and robustness in recognizing nested entities were more pronounced, particularly in the challenging CMeEE V2 dataset. In conclusion, while nested entity recognition continues to present significant challenges in NER tasks, constructing high-quality datasets and adopting a MFGFF approach can significantly enhance the model performance. The MFGFF method, with its superior stability and robustness, outperformed the fine-tuning-only strategy in recognizing nested entities, demonstrating both the effectiveness and potential application value of our proposed method. ### Comparative Analysis of Pre-Rained Models

The choice of a pre-trained model for embedding is crucial to the recognition effectiveness of NER models. We compared the performance of five pre-trained models\(-\)BERT, RoBERTa, XLNet, MCBERT, and ERNIE\(-\)as illustrated in Figure 11.

RoBERTa outperformed the other models across all metrics, particularly in recall (92.18%) and F1 score (91.98%), where it led significantly.