scholarly journals Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0216046 ◽  
Author(s):  
Xishuang Dong ◽  
Shanta Chowdhury ◽  
Lijun Qian ◽  
Xiangfang Li ◽  
Yi Guan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electronic Medical Records ◽  
Transfer Learning ◽  
Medical Records ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity

scholarly journals Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Lejun Gong ◽  
Zhifei Zhang ◽  
Shiqi Chen
Keyword(s):  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Named Entity Recognition ◽  
Clinical Entity ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Recognition Model ◽  
Named Entity ◽  
Model Based

Background. Clinical named entity recognition is the basic task of mining electronic medical records text, which are with some challenges containing the language features of Chinese electronic medical records text with many compound entities, serious missing sentence components, and unclear entity boundary. Moreover, the corpus of Chinese electronic medical records is difficult to obtain. Methods. Aiming at these characteristics of Chinese electronic medical records, this study proposed a Chinese clinical entity recognition model based on deep learning pretraining. The model used word embedding from domain corpus and fine-tuning of entity recognition model pretrained by relevant corpus. Then BiLSTM and Transformer are, respectively, used as feature extractors to identify four types of clinical entities including diseases, symptoms, drugs, and operations from the text of Chinese electronic medical records. Results. 75.06% Macro-P, 76.40% Macro-R, and 75.72% Macro-F1 aiming at test dataset could be achieved. These experiments show that the Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition effect. Conclusions. These experiments show that the proposed Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition performance.

scholarly journals An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records

2019 ◽  
Vol 19 (S5) ◽  
Author(s):  
Luqi Li ◽  
Jie Zhao ◽  
Li Hou ◽  
Yunkai Zhai ◽  
Jinming Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Named Entity Recognition ◽  
Attention Mechanism ◽  
Entity Recognition ◽  
Long Distance ◽  
Named Entity ◽  
Text Features

Abstract Background Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical records (EMRs), there are still many difficulties in clinical named entity recognition of Chinese EMRs. It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus. Methods From the perspective of deep learning, we integrated the attention mechanism into neural network, and proposed an improved clinical named entity recognition method for Chinese electronic medical records called BiLSTM-Att-CRF, which could capture more useful information of the context and avoid the problem of missing information caused by long-distance factors. In addition, medical dictionaries and part-of-speech (POS) features were also introduced to improve the performance of the model. Results Based on China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2018 Chinese EMRs corpus, our BiLSTM-Att-CRF model finally achieved better performance than other widely-used models without additional features(F1-measure of 85.4% in CCKS 2018, F1-measure of 90.29% in CCKS 2017), and achieved the best performance with POS and dictionary features (F1-measure of 86.11% in CCKS 2018, F1-measure of 90.48% in CCKS 2017). In particular, the BiLSTM-Att-CRF model had significant effect on the improvement of Recall. Conclusions Our work preliminarily confirmed the validity of attention mechanism in discovering key information and mining text features, which might provide useful ideas for future research in clinical named entity recognition of Chinese electronic medical records. In the future, we will explore the deeper application of attention mechanism in neural network.

scholarly journals Overview of CCKS 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records

2021 ◽  
pp. 1-13
Author(s):  
Xia Li ◽  
Qinghua Wen ◽  
Zengtao Jiao ◽  
Jiangtao Zhang
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Named Entity Recognition ◽  
Event Extraction ◽  
Entity Recognition ◽  
Language Models ◽  
Data Sets ◽  
External Resources ◽  
Named Entity ◽  
Evaluation Task

Abstract The China Conference on Knowledge Graph and Semantic Computing (CCKS) 2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical records. Two annotated data sets and some other additional resources for these two subtasks were provided for participators. This evaluation competition attracted 354 teams and 46 of them successfully submitted the valid results. The pre-trained language models are widely applied in this evaluation task. Data argumentation and external resources are also helpful.

scholarly journals Information Extraction from Electronic Medical Records Using Multitask Recurrent Neural Network with Contextual Word Embedding

2019 ◽  
Vol 9 (18) ◽  
pp. 3658 ◽  
Author(s):  
Jianliang Yang ◽  
Yuenan Liu ◽  
Minghui Qian ◽  
Chenghua Guan ◽  
Xiangfei Yuan
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Large Scale ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
Language Models ◽  
Named Entity

Clinical named entity recognition is an essential task for humans to analyze large-scale electronic medical records efficiently. Traditional rule-based solutions need considerable human effort to build rules and dictionaries; machine learning-based solutions need laborious feature engineering. For the moment, deep learning solutions like Long Short-term Memory with Conditional Random Field (LSTM–CRF) achieved considerable performance in many datasets. In this paper, we developed a multitask attention-based bidirectional LSTM–CRF (Att-biLSTM–CRF) model with pretrained Embeddings from Language Models (ELMo) in order to achieve better performance. In the multitask system, an additional task named entity discovery was designed to enhance the model’s perception of unknown entities. Experiments were conducted on the 2010 Informatics for Integrating Biology & the Bedside/Veterans Affairs (I2B2/VA) dataset. Experimental results show that our model outperforms the state-of-the-art solution both on the single model and ensemble model. Our work proposes an approach to improve the recall in the clinical named entity recognition task based on the multitask mechanism.

Disease Named Entity Recognition in Medical Records: Analysis of Transfer Learning between Different Hospital Departments (Preprint)

2021 ◽  
Author(s):  
Jong-Kang Lee ◽  
Jue-Ni Huang ◽  
Kun-Ju Lin ◽  
Richard Tzong-Han Tsai
Keyword(s):  
Transfer Learning ◽  
Medical Records ◽  
Named Entity Recognition ◽  
Model Performance ◽  
Entity Recognition ◽  
Similarity Metrics ◽  
Medical Department ◽  
Named Entity ◽  
Data Source ◽  
Mimic Iii

BACKGROUND Electronic records provide rich clinical information for biomedical text mining. However, a system developed on one hospital department may not generalize to other departments. Here, we use hospital medical records as a research data source and explore the heterogeneous problem posed by different hospital departments. OBJECTIVE We use MIMIC-III hospital medical records as the research data source. We collaborate with medical experts to annotate the data, with 328 records being included in analyses. Disease named entity recognition (NER), which helps medical experts in consolidating diagnoses, is undertaken as a case study. METHODS To compare heterogeneity of medical records across departments, we access text from multiple departments and employ the similarity metrics. We apply transfer learning to NER in different departments’ records and test the correlation between performance and similarity metrics. We use TF-IDF cosine similarity of the named entities as our similarity metric. We use three pretrained model on the disease NER task to valid the consistency of the result. RESULTS The disease NER dataset we release consists of 328 medical records from MIMIC-III, with 95629 sentences and 8884 disease mentions in total. The inter annotator agreement Cohen’s kappa coefficient is 0.86. Similarity metrics support that medical records from different departments are heterogeneous, ranges from 0.1004 to 0.3541 compare to Medical department. In the transfer learning task using the Medical department as the training set, F1 score performs in three pretrained models average from 0.847 to 0.863. F1 scores correlate with similarity metrics with Spearman’s coefficient of 0.4285. CONCLUSIONS We propose a disease NER dataset based on medical records from MIMIC-III and demonstrate the effectiveness of transfer learning using BERT. Similarity metrics reveal noticeable heterogeneity between department records. The deep learning-based transfer learning method demonstrates good ability to generalize across departments and achieve decent NER performance thus eliminates the concern that training material from one hospital might compromise model performance when applied to another. However, the model performance does not show high correlation to the departments’ similarity.

scholarly journals A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qiuli Qin ◽  
Shuang Zhao ◽  
Chunmei Liu
Keyword(s):  
Cerebrovascular Disease ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Body Parts ◽  
Named Entity ◽  
Excellent Research

Because of difficulty processing the electronic medical record data of patients with cerebrovascular disease, there is little mature recognition technology capable of identifying the named entity of cerebrovascular disease. Excellent research results have been achieved in the field of named entity recognition (NER), but there are several problems in the pre processing of Chinese named entities that have multiple meanings, of which neglecting the combination of contextual information is one. Therefore, to extract five categories of key entity information for diseases, symptoms, body parts, medical examinations, and treatment in electronic medical records, this paper proposes the use of a BERT-BiGRU-CRF named entity recognition method, which is applied to the field of cerebrovascular diseases. The BERT layer first converts the electronic medical record text into a low-dimensional vector, then uses this vector as the input to the BiGRU layer to capture contextual features, and finally uses conditional random fields (CRFs) to capture the dependency between adjacent tags. The experimental results show that the F1 score of the model reaches 90.38%.

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