# A Survey of Deep Learning for Electronic Health Records
## 5 Deep Learning Applications in EHR
### Information Extraction
#### 5.1.1 Named Entity Recognition (NER)
Medical related: Medical information, the second-largest task in the medical text NER, is more complicated than disease name because it includes several subtasks such as drug name, frequency of dispensing (Dr-Freq) and route of dispensing (Dr-Route), drug dose (Dr dose), duration of drug use (Dr-Dur), and adverse drug reaction events (Dr-ADE). These subtasks are closely tied to medical data and are based on specific standards and specifications, such as the international standard HL7 and the Fast Healthcare Interoperability Resources (FHIR). The NER task in clinical reports has a higher entity type complexity, but in medical reports or medical papers, NER must cope with distinct corpora and text types of disease recognition tasks. According to the survey, when both methods are used with the same corpus, the deep learning method performs better than the non-deep learning method. In terms of the current performance of multiple NER models in medical health data, the i2b2/n2c2 corpus has practically all of the best results. [PERSON] et al. [51] proposed a neural network architecture (Bi-LSTM + CRF) in the field of drug name recognition, which can model the target drug information and the associated sequence utilizing label characteristics, and the JOINT technique was derived on this premise. Using a new transformation, the JOINT approach generates drug-focused sequences from annotated texts. Some rules are changed in their study to boost recognition performance even further, and the F1-score is increased to almost 95% after postprocessing. There are several great models for determining drug distribution routes, and the difference between them and the estimated ideal system is only approximately 1%. The current performance in identifying hazardous drug response events is dismal, with a deviation of more than 10% from the predicted value, implying that identifying adverse drug reaction events is the most difficult task. On this data set, the F1-score of the DLADE system [52], which integrates Bi-LSTM and CRF into a deep network system, reaches 64 percent, whereas the F1-score of the model proposed on similar task [51] is 53 percent. The F1-score generated by the deep learning model has reached an ideal range in additional tasks, such as drug frequency recognition, medication delivery route, and drug dose identification. In general, adverse drug response events are the most difficult types to adequately represent in plain language in NER tasks connected to drug information, which is why current deep learning in the subtask is unable to achieve good performance. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Dataset** & **F1-Scores \%** \\ \hline \multirow{3}{*}{Bio-BERTv1.1 (+PubMed) [46]} & NCBI Disease & 89.7 \\  & 20102b2/ V A & 86.73 \\  & BC5 CDR & 87.15 \\ \hline \multirow{6}{*}{MTM-CW [48]} & BC2 GM (Exact) & 80.74 \(\pm\) 0.04 \\  & BC2 GM (Alternative) & 89.06 \(\pm\) 0.32 \\ \cline{1-1}  & BC4 CHEMD & 89.37 \(\pm\) 0.07 \\ \cline{1-1}  & BC5 CDR & 88.78 \(\pm\) 0.12 \\ \cline{1-1}  & NCBI-Disease & 86.14 \(\pm\) 0.31 \\ \cline{1-1}  & JNLPBA & 73.52 \(\pm\) 0.03 \\ \hline \multirow{3}{*}{MTL-MEN& MER feedback + Bi-LSTM -CNNs-CRF [49]} & NCBI Disease & 88.23 \\ \cline{1-1}  & BC5 CDR & 89.17 \\ \hline \multirow{3}{*}{Bidirectional Language Modeling [47]} & NCBI Disease & 87.34 \\ \cline{1-1}  & BC5 CDR & 89.28 \\ \cline{1-1}  & BC2 GM & 81.69 \\ \cline{1-1}  & JNLPBA & 75.03 \\ \hline \multirow{3}{*}{KA-NER [50]} & NCBI-Disease & 86.12 \\ \cline{1-1}  & CoNLL03 & 92.40 \\ \cline{1-1}  & Genia & 75.59 \\ \cline{1-1}  & SEC & 85.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: F1-scores of each model on different datasets.