scholarly journals An Improved Method for Named Entity Recognition and Its Application to CEMR

2019 ◽  
Vol 11 (9) ◽  
pp. 185
Author(s):  
Ming Gao ◽  
Qifeng Xiao ◽  
Shaochun Wu ◽  
Kun Deng
Keyword(s):  
Word Order ◽  
Medical Records ◽  
Semantic Information ◽  
Short Term Memory ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Local Context ◽  
Network Computing ◽  
Named Entity ◽  
Gpu Parallelism

Named Entity Recognition (NER) on Clinical Electronic Medical Records (CEMR) is a fundamental step in extracting disease knowledge by identifying specific entity terms such as diseases, symptoms, etc. However, the state-of-the-art NER methods based on Long Short-Term Memory (LSTM) fail to exploit GPU parallelism fully under the massive medical records. Although a novel NER method based on Iterated Dilated CNNs (ID-CNNs) can accelerate network computing, it tends to ignore the word-order feature and semantic information of the current word. In order to enhance the performance of ID-CNNs-based models on NER tasks, an attention-based ID-CNNs-CRF model, which combines the word-order feature and local context, is proposed. Firstly, position embedding is utilized to fuse word-order information. Secondly, the ID-CNNs architecture is used to extract global semantic information rapidly. Simultaneously, the attention mechanism is employed to pay attention to the local context. Finally, we apply the CRF to obtain the optimal tag sequence. Experiments conducted on two CEMR datasets show that our model outperforms traditional ones. The F1-scores of 94.55% and 91.17% are obtained respectively on these two datasets, and both are better than LSTM-based models.

scholarly journals GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition

2019 ◽  
Vol 33 ◽  
pp. 6236-6243 ◽  
Author(s):  
Hui Chen ◽  
Zijia Lin ◽  
Guiguang Ding ◽  
Jianguang Lou ◽  
Yusen Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Local Context ◽  
Named Entity ◽  
Entire Sentence ◽  
Context Features ◽  
Gpu Parallelism

The dominant approaches for named entity recognitionm (NER) mostly adopt complex recurrent neural networks (RNN), e.g., long-short-term-memory (LSTM). However, RNNs are limited by their recurrent nature in terms of computational efficiency. In contrast, convolutional neural networks (CNN) can fully exploit the GPU parallelism with their feedforward architectures. However, little attention has been paid to performing NER with CNNs, mainly owing to their difficulties in capturing the long-term context information in a sequence. In this paper, we propose a simple but effective CNN-based network for NER, i.e., gated relation network (GRN), which is more capable than common CNNs in capturing long-term context. Specifically, in GRN we firstly employ CNNs to explore the local context features of each word. Then we model the relations between words and use them as gates to fuse local context features into global ones for predicting labels. Without using recurrent layers that process a sentence in a sequential manner, our GRN allows computations to be performed in parallel across the entire sentence. Experiments on two benchmark NER datasets (i.e., CoNLL2003 and Ontonotes 5.0) show that, our proposed GRN can achieve state-of-the-art performance with or without external knowledge. It also enjoys lower time costs to train and test.

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.

scholarly journals Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations (Preprint)

2020 ◽  
Author(s):  
Yongbin Li ◽  
Xiaohua Wang ◽  
Linhu Hui ◽  
Liping Zou ◽  
Hongjin Li ◽  
...  
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Short Term Memory ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Models ◽  
Short Term ◽  
Term Memory ◽  
Named Entity ◽  
Long Short Term Memory

BACKGROUND Clinical named entity recognition (CNER), whose goal is to automatically identify clinical entities in electronic medical records (EMRs), is an important research direction of clinical text data mining and information extraction. The promotion of CNER can provide support for clinical decision making and medical knowledge base construction, which could then improve overall medical quality. Compared with English CNER, and due to the complexity of Chinese word segmentation and grammar, Chinese CNER was implemented later and is more challenging. OBJECTIVE With the development of distributed representation and deep learning, a series of models have been applied in Chinese CNER. Different from the English version, Chinese CNER is mainly divided into character-based and word-based methods that cannot make comprehensive use of EMR information and cannot solve the problem of ambiguity in word representation. METHODS In this paper, we propose a lattice long short-term memory (LSTM) model combined with a variant contextualized character representation and a conditional random field (CRF) layer for Chinese CNER: the Embeddings from Language Models (ELMo)-lattice-LSTM-CRF model. The lattice LSTM model can effectively utilize the information from characters and words in Chinese EMRs; in addition, the variant ELMo model uses Chinese characters as input instead of the character-encoding layer of the ELMo model, so as to learn domain-specific contextualized character embeddings. RESULTS We evaluated our method using two Chinese CNER datasets from the China Conference on Knowledge Graph and Semantic Computing (CCKS): the CCKS-2017 CNER dataset and the CCKS-2019 CNER dataset. We obtained F1 scores of 90.13% and 85.02% on the test sets of these two datasets, respectively. CONCLUSIONS Our results show that our proposed method is effective in Chinese CNER. In addition, the results of our experiments show that variant contextualized character representations can significantly improve the performance of the model.

scholarly journals Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations

10.2196/19848 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e19848
Author(s):  
Yongbin Li ◽  
Xiaohua Wang ◽  
Linhu Hui ◽  
Liping Zou ◽  
Hongjin Li ◽  
...  
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Short Term Memory ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Language Models ◽  
Short Term ◽  
Term Memory ◽  
Named Entity ◽  
Long Short Term Memory

Background Clinical named entity recognition (CNER), whose goal is to automatically identify clinical entities in electronic medical records (EMRs), is an important research direction of clinical text data mining and information extraction. The promotion of CNER can provide support for clinical decision making and medical knowledge base construction, which could then improve overall medical quality. Compared with English CNER, and due to the complexity of Chinese word segmentation and grammar, Chinese CNER was implemented later and is more challenging. Objective With the development of distributed representation and deep learning, a series of models have been applied in Chinese CNER. Different from the English version, Chinese CNER is mainly divided into character-based and word-based methods that cannot make comprehensive use of EMR information and cannot solve the problem of ambiguity in word representation. Methods In this paper, we propose a lattice long short-term memory (LSTM) model combined with a variant contextualized character representation and a conditional random field (CRF) layer for Chinese CNER: the Embeddings from Language Models (ELMo)-lattice-LSTM-CRF model. The lattice LSTM model can effectively utilize the information from characters and words in Chinese EMRs; in addition, the variant ELMo model uses Chinese characters as input instead of the character-encoding layer of the ELMo model, so as to learn domain-specific contextualized character embeddings. Results We evaluated our method using two Chinese CNER datasets from the China Conference on Knowledge Graph and Semantic Computing (CCKS): the CCKS-2017 CNER dataset and the CCKS-2019 CNER dataset. We obtained F1 scores of 90.13% and 85.02% on the test sets of these two datasets, respectively. Conclusions Our results show that our proposed method is effective in Chinese CNER. In addition, the results of our experiments show that variant contextualized character representations can significantly improve the performance of the model.

scholarly journals CNN-Based Chinese NER with Lexicon Rethinking

2019 ◽  
Author(s):  
Tao Gui ◽  
Ruotian Ma ◽  
Qi Zhang ◽  
Lujun Zhao ◽  
Yu-Gang Jiang ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Great Success ◽  
Short Term ◽  
Named Entity ◽  
Word Level ◽  
Long Short Term Memory ◽  
High Level ◽  
Gpu Parallelism

Character-level Chinese named entity recognition (NER) that applies long short-term memory (LSTM) to incorporate lexicons has achieved great success. However, this method fails to fully exploit GPU parallelism and candidate lexicons can conflict. In this work, we propose a faster alternative to Chinese NER: a convolutional neural network (CNN)-based method that incorporates lexicons using a rethinking mechanism. The proposed method can model all the characters and potential words that match the sentence in parallel. In addition, the rethinking mechanism can address the word conflict by feeding back the high-level features to refine the networks. Experimental results on four datasets show that the proposed method can achieve better performance than both word-level and character-level baseline methods. In addition, the proposed method performs up to 3.21 times faster than state-of-the-art methods, while realizing better performance.

A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records

2021 ◽  
Vol 20 (2) ◽  
pp. 1-12
Author(s):  
Yu Wang ◽  
Yining Sun ◽  
Zuchang Ma ◽  
Lisheng Gao ◽  
Yang Xu
Keyword(s):  
Neural Network ◽  
Electronic Medical Records ◽  
Hybrid Model ◽  
Medical Records ◽  
Medical Information ◽  
Short Term Memory ◽  
Clinical Symptoms ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity

Electronic medical records (EMRs) contain valuable information about the patients, such as clinical symptoms, diagnostic results, and medications. Named entity recognition (NER) aims to recognize entities from unstructured text, which is the initial step toward the semantic understanding of the EMRs. Extracting medical information from Chinese EMRs could be a more complicated task because of the difference between English and Chinese. Some researchers have noticed the importance of Chinese NER and used the recurrent neural network or convolutional neural network (CNN) to deal with this task. However, it is interesting to know whether the performance could be improved if the advantages of the RNN and CNN can be both utilized. Moreover, RoBERTa-WWM, as a pre-training model, can generate the embeddings with word-level features, which is more suitable for Chinese NER compared with Word2Vec. In this article, we propose a hybrid model. This model first obtains the entities identified by bidirectional long short-term memory and CNN, respectively, and then uses two hybrid strategies to output the final results relying on these entities. We also conduct experiments on raw medical records from real hospitals. This dataset is provided by the China Conference on Knowledge Graph and Semantic Computing in 2019 (CCKS 2019). Results demonstrate that the hybrid model can improve performance significantly.

scholarly journals Research on Named Entity Recognition of Electronic Medical Records Based on RoBERTa and Radical-Level Feature

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yue Wu ◽  
Jie Huang ◽  
Caie Xu ◽  
Huilin Zheng ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Medical Records ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Level Information ◽  
Long Short Term Memory ◽  
The Relationship

Clinical named entity recognition (CNER) identifies entities from unstructured medical records and classifies them into predefined categories. It is of great significance for follow-up clinical studies. Most of the existing CNER methods fail to give enough thought to Chinese radical-level characteristics and the specialty of the Chinese field. This paper proposes the Ra-RC model, which combines radical features and a deep learning structure to fix this problem. A bidirectional encoder representation of transformer (RoBERTa) is utilized to learn medical features thoroughly. Simultaneously, we use the bidirectional long short-term memory (BiLSTM) network to extract radical-level information to capture the internal relevance of characteristics and stitch the eigenvectors generated by RoBERTa. In addition, the relationship between labels is considered to obtain the optimal tag sequence by applying conditional random field (CRF). The experimental results demonstrate that the proposed Ra-RC model achieves F1 score 93.26% and 82.87% on the CCKS2017 and CCKS2019 datasets, respectively.

Sign in / Sign up

Export Citation Format

Share Document