# A Survey of Deep Learning for Electronic Health Records
## 5 Deep Learning Applications in EHR
### Information Extraction
#### 5.1.1 Named Entity Recognition (NER)  
The basic goal of the EHR entity recognition task is to recognize medical-named entities in medical texts, such as disease names, medicine names, and related technical semantic data. Disease name: The basic purpose of NER in medical text information is disease name recognition. Medical publications and clinical reports make up the majority of the corpus. The disease-related text is mapped to the same semantic type by NER. [PERSON] et al. [44] have explored the use of natural language technology in chronic disease clinical records, concluding that shallow classifiers and rule-based techniques are now the most popular. They also recognized that, while these algorithms can extract useful medical information from text, they essentially eliminated the ability to extract more intricate correlations from medical texts. [PERSON] et al. [43] focused on using natural language processing to assess the symptom information in the free text of EHR as a potent base for illness diagnosis, although deep learning technology is not frequently used in this field. [PERSON] et al. [45] looked into how NLP is used in oncology and cancer characterization. Furthermore, computer scientists and medical professionals must collaborate to boost the usefulness and applicability of NLP technology in this industry from a disciplinary integration standpoint. At present, the best performance in the recognition of the disease name recognition is close to 90% (F1-score). [PERSON] et al. [46] proposed Bio-BERT, the first BERT model focused on specialized disciplines and demonstrated that pre-training BERT on the biomedical corpus considerably increased its performance. This model not only retains a high-level performance but it also has a considerable standard of generalization (only a little modification is needed for different tasks). [PERSON] et al. [47] have also conducted relevant research in this area, obtaining various degrees of neural networks based on general neural network improvement. The character-level convolutional neural network (CNN) layer, word embedding layer, word-level Bi-LSTM layer, decoder layer, and sentence-level label prediction layer are all part of the Bidirectional Language Model made up of these upgraded neural networks. [PERSON] et al. [48] suggested a multi-task learning framework (Bi-LSTM + CRF) for Bio-NER to focus on the training data of various types of entities and improve each entity's performance. They argued that MTM-CW was the best-performing multi-task model since it shares the most information among tasks. [PERSON] et al. [49] also suggested a multi-task learning paradigm that can set up parallel multi-tasks while maintaining bidirectional task support. These studies have dealt with data scarcity to some extent; however, as indicated in Table 3, the best performance in specific task performance is still [46]. The recently proposed KA-NER by [PERSON] et al. [50] incorporates a relevant knowledge base compared to most current NER models that focus only on contextual content or artificially added features. The KA-NER model performs well on multiple datasets and has a high generalization capability due to the average F1-score of 84.82% achieved by this model on multiple datasets. As shown in Table 2, there are many indications in the evaluation of NER outcomes, and the scores obtained by the same model on different indicators may be inconsistent, and when utilizing different data sets, model performance may also improve or decrease. At the moment, the system architecture proposed by [49] has the most stable comprehensive score, and the systems demonstrated by [46] have the best performance of deep learning systems, with a performance gap of roughly 7.6% compared to the similar non-deep learning system.