# Deep Learning Framework for Advanced De-Identification of Protected Health Information
## 4 Results and Discussion
### Comparison Between Our Findings with Other Studies  
Clinical data processing has been improved using Bi-LSTM-CRF techniques for PHI de-identification in EHRs. This method, which combines the advantages of sequence labeling and deep learning, improves the accuracy and efficiency of identifying sensitive information. In [34], the authors used the Rennes University Hospital's EHRs to create a French de-identification dataset. The distribution of entities in the training and test sets was consistent, and the dataset included extensive personal information. A Bi-LSTM + CRF model was assessed using manually annotated clinical reports in conjunction with Flair  
Figure 8: Evaluation results for tags with support less than 300. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**B-Tag**} & \multicolumn{3}{c}{**I-Tag**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline Age & 0.91 & 0.83 & 0.87 & 0 & 0 & 0 \\ \hline City & 0.27 & 0.36 & 0.31 & 0.33 & 0.09 & 0.14 \\ \hline Country & 0.33 & 0.14 & 0.20 & 0 & 0 & 0 \\ \hline Date & 0.46 & 0.71 & 0.56 & 0.85 & 0.73 & 0.78 \\ \hline Doctor & 0.51 & 0.69 & 0.59 & 0.62 & 0.63 & 0.62 \\ \hline Hospital & 0.41 & 0.50 & 0.45 & 0.83 & 0.45 & 0.59 \\ \hline Patient & 0.53 & 0.43 & 0.47 & 0.47 & 0.24 & 0.32 \\ \hline Profession & 0.28 & 0.22 & 0.25 & 0.65 & 0.24 & 0.35 \\ \hline Street & 0.62 & 0.67 & 0.65 & 0.82 & 0.61 & 0.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of B-tag and I-tag results using Bi-LSTM-CRF model. and FastText word embeddings. The model outperformed other tested models and demonstrated the efficacy of this strategy for de-identifying sensitive information with a high F1-score of 96.96%. To identify text that should be classified as sensitive due to privacy concerns, the authors in [35] used three different models: Bi-LSTM, Bi-LSTM with attention, and BERT base models. The words used can disclose important details about a patient's character and personal life, even though it may not at first appear to be sensitive to privacy concerns. Clinical data include hidden features that may cause privacy problems in addition to demographic data. Approximately 92% accuracy was achieved by enhancing the Bi-LSTM model with an attention layer that highlights crucial words that are important for categorization. The dataset comprised 206,926 phrases, of which 80% was used for training and the remaining 20% for testing. Using the Bi-LSTM model alone, the dataset yielded an accuracy of about 90%. In [36], the authors presented a novel multi-medical entity recognition approach combining BART, Bi-LSTM, and CRF using a fusion strategy. EHRs were first cleaned, encoded, and segmented. Then, semantic representations were dynamically merged using the BART model. Subsequently, sequential data were captured by the Bi-LSTM network, and CRF was utilized to decode and produce multi-task entity recognition outcomes. The approach produced an average precision, recall, and F1-score of 0.880, 0.887, and 0.883, respectively. Based on the BiLSTM-CRF deep learning model, the authors in [37] presented the Bi-RNN-LSTM-RNN-CRF electronic medical record named entity recognition model. A bidirectional RNN-LSTM-RNN layer was trained by first gathering an electronic medical record dataset and then utilizing a word vector tool to turn the characters into vectors. After that, a CRF layer received the training data to compute the loss function and produce predictions. To compare the two models, identical procedures were carried out using the conventional BiLSTM-CRF model. According to the experimental results, the Bi-RNN-LSTM-RNN-CRF model outperformed the BiLSTM-CRF model in recognition, with an F1-score of 97.80%. [PERSON] et al. [19] used a manually annotated corpus to refine the bidirectional transformer pre-training model BERT by the BIOES (Begin Inside Outside End Single) standard.