An automatic grading system for electronic medical records with neural network

<abstract> <p>Named entities are the main carriers of relevant medical knowledge in Electronic Medical Records (EMR). Clinical electronic medical records lead to problems such as word segmentation ambiguity and polysemy due to the specificity of Chinese language structure, so a Clinical Named Entity Recognition (CNER) model based on multi-head self-attention combined with BILSTM neural network and Conditional Random Fields is proposed. Firstly, the pre-trained language model organically combines char vectors and word vectors for the text sequences of the original dataset. The sequences are then fed into the parallel structure of the multi-head self-attention module and the BILSTM neural network module, respectively. By splicing the output of the neural network module to obtain multi-level information such as contextual information and feature association weights. Finally, entity annotation is performed by CRF. The results of the multiple comparison experiments show that the structure of the proposed model is very reasonable and robust, and it can effectively improve the Chinese CNER model. The model can extract multi-level and more comprehensive text features, compensate for the defect of long-distance dependency loss, with better applicability and recognition performance.</p> </abstract>

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

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0933-6 ◽

2019 ◽

Vol 19 (S5) ◽

Cited By ~ 6

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.

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Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17051614 ◽

2020 ◽

Vol 17 (5) ◽

pp. 1614

Author(s):

Yan Gao ◽

Yandong Wang ◽

Patrick Wang ◽

Lei Gu

Keyword(s):

Neural Network ◽

Electronic Medical Records ◽

Medical Records ◽

Attention Mechanism ◽

Entity Recognition ◽

Medical Decision ◽

Word Embeddings ◽

Entity Extraction ◽

Medical Entity ◽

Word Attention

The resident admit notes (RANs) in electronic medical records (EMRs) is first-hand information to study the patient’s condition. Medical entity extraction of RANs is an important task to get disease information for medical decision-making. For Chinese electronic medical records, each medical entity contains not only word information but also rich character information. Effective combination of words and characters is very important for medical entity extraction. We propose a medical entity recognition model based on a character and word attention-enhanced (CWAE) neural network for Chinese RANs. In our model, word embeddings and character-based embeddings are obtained through character-enhanced word embedding (CWE) model and Convolutional Neural Network (CNN) model. Then attention mechanism combines the character-based embeddings and word embeddings together, which significantly improves the expression ability of words. The new word embeddings obtained by the attention mechanism are taken as the input to bidirectional long short-term memory (BI-LSTM) and conditional random field (CRF) to extract entities. We extracted nine types of key medical entities from Chinese RANs and evaluated our model. The proposed method was compared with two traditional machine learning methods CRF, support vector machine (SVM), and the related deep learning models. The result shows that our model has better performance, and the result of our model reaches 94.44% in the F1-score.

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Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0775-2 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 9

Author(s):

Merijn Beeksma ◽

Suzan Verberne ◽

Antal van den Bosch ◽

Enny Das ◽

Iris Hendrickx ◽

...

Keyword(s):

Neural Network ◽

Life Expectancy ◽

Electronic Medical Records ◽

Recurrent Neural Network ◽

Medical Records ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3436819 ◽

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.

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Using Artificial Intelligence Technology to Solve the Electronic Health Service by Processing the Online Case Information

Journal of Healthcare Engineering ◽

10.1155/2021/9637018 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Guoxiang Xu ◽

Hao Jin

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Health Services ◽

Electronic Medical Records ◽

Neural Network Model ◽

Medical Records ◽

Accuracy Rate ◽

Artificial Intelligence Technology ◽

Electronic Health ◽

Fully Connected

With the continuous improvement of economic level and the continuous development of science and technology in China, information technology has begun to integrate into all walks of life. Medical units have begun to change from the traditional medical system to the intelligent system, and the processing of online case information has become an important component of medical informationization. To improve the efficiency of dealing with online case information, this study proposes to establish a fully connected neural network model to deal with online cases. Using jieba word segmentation tool and data preprocessing technology, the data of electronic medical records are sorted out, and the data are quantified using Word2Vec and other tools, and the data on electronic medical records are converted into one-hot binary variables. The quantified data are trained into a fully connected neural model, and the accuracy rate is about 88%. It is compared with naive Bayes and decision tree classification methods, and then a comparative experiment is carried out by solving e-health services in different ways. The results show that the fully connected neural network model has the best classification effect: the highest accuracy rate is about 93.7%, the highest precision rate is about 94.0%, the highest recall rate is about 95.3%, and the highest F1 score is about 94.6%. However, using artificial intelligence technology to solve electronic health services has great advantages, among which efficiency, assistance, and service satisfaction are all higher than 90%, which provides favorable technical support for electronic health services.

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