Chinese Electronic Medical Records Research Articles

Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study

BackgroundWith the increasing incidences and mortality of digestive system tumor diseases in China, ways to use clinical experience data in Chinese electronic medical records (CEMRs) to determine potentially effective relationships between diagnosis and treatment have become a priority. As an important part of artificial intelligence, a knowledge graph is a powerful tool for information processing and knowledge organization that provides an ideal means to solve this problem.ObjectiveThis study aimed to construct a semantic-driven digestive system tumor knowledge graph (DSTKG) to represent the knowledge in CEMRs with fine granularity and semantics.MethodsThis paper focuses on the knowledge graph schema and semantic relationships that were the main challenges for constructing a Chinese tumor knowledge graph. The DSTKG was developed through a multistep procedure. As an initial step, a complete DSTKG construction framework based on CEMRs was proposed. Then, this research built a knowledge graph schema containing 7 classes and 16 kinds of semantic relationships and accomplished the DSTKG by knowledge extraction, named entity linking, and drawing the knowledge graph. Finally, the quality of the DSTKG was evaluated from 3 aspects: data layer, schema layer, and application layer.ResultsExperts agreed that the DSTKG was good overall (mean score 4.20). Especially for the aspects of “rationality of schema structure,” “scalability,” and “readability of results,” the DSTKG performed well, with scores of 4.72, 4.67, and 4.69, respectively, which were much higher than the average. However, the small amount of data in the DSTKG negatively affected its “practicability” score. Compared with other Chinese tumor knowledge graphs, the DSTKG can represent more granular entities, properties, and semantic relationships. In addition, the DSTKG was flexible, allowing personalized customization to meet the designer's focus on specific interests in the digestive system tumor.ConclusionsWe constructed a granular semantic DSTKG. It could provide guidance for the construction of a tumor knowledge graph and provide a preliminary step for the intelligent application of knowledge graphs based on CEMRs. Additional data sources and stronger research on assertion classification are needed to gain insight into the DSTKG’s potential.

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

BackgroundClinical 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.ObjectiveWith 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.MethodsIn 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.ResultsWe 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.ConclusionsOur 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.

Learning an expandable EMR-based medical knowledge network to enhance clinical diagnosis.

Electronic medical records (EMRs) contain a wealth of knowledge that can be used to assist doctors in making clinical decisions like disease diagnosis. Constructing a medical knowledge network (MKN) to link medical concepts in EMRs is an effective way to manage this knowledge. The quality of the diagnostic result made by MKN-based clinical decision support system depends on the accuracy of medical knowledge and the completeness of the network. However, collecting knowledge is a long-lasting and cumulative process, which means it's hard to construct a complete MKN with limited data. This study was conducted with the objective of developing an expandable EMR-based MKN to enhance capabilities in making an initial clinical diagnosis. A network of symptom-indicate-disease knowledge in 992 Chinese EMRs (CEMRs) was manually constructed as Original-MKN, and an incremental expansion framework was applied to it to obtain an expandable MKN based on new CEMRs. The framework was composed by: (1) integrating external knowledge extracted from the medical information websites and (2) mining potential knowledge with new EMRs. The framework also adopts a diagnosis-driven learning method to estimate the effectiveness of each knowledge in clinical practice. Experimental results indicate that our expanded MKN achieves a precision of 0.837 for a recall of 0.719 in clinical diagnosis, which outperforms Original-MKN and four classical machine learning methods. Furthermore, both external medical knowledge and potential medical knowledge benefit MKN expansion and disease diagnosis. The proposed incremental expansion framework sustains the MKN learning new knowledge.

Knowledge-Enhanced Relation Extraction for Chinese EMRs

The growing demand for the meaningful use of electronic medical records has led to great interest in medical entities and relation extraction technologies. Most existing methods perform relation extraction based on manually labeled documents and rarely consider incorporating knowledge graphs that include rich, valuable structured knowledge, which will cause semantic ambiguities. To address this problem, we propose a knowledge-enhanced relation extraction (KERE) model. First, we extract knowledge information from the knowledge graph to generate a knowledge-guided word embedding. Then, the lexical features are considered supplementary information for semantic understanding. Experiments on real-world datasets show that the KERE model achieves important improvements in a biomedical relation extraction task.

Enhanced character-level deep convolutional neural networks for cardiovascular disease prediction

BackgroundElectronic medical records contain a variety of valuable medical information for patients. So, when we are able to recognize and extract risk factors for disease from EMRs of patients with cardiovascular disease (CVD), and are able to use them to predict CVD, we have the ability to automatically process clinical texts, resulting in an improved accuracy of supporting doctors for the clinical diagnosis of CVD. In the case where CVD is becoming more worldwide, predictive CVD based on EMRs has been studied by many researchers to address this important aspect of improving diagnostic efficiency.MethodsThis paper proposes an Enhanced Character-level Deep Convolutional Neural Networks (EnDCNN) model for cardiovascular disease prediction.ResultsOn the manually annotated Chinese EMRs corpus, our risk factor identification extraction model achieved 0.9073 of F-score, our prediction model achieved 0.9516 of F-score, and the prediction result is better than the most previous methods.ConclusionsThe character-level model based on text region embedding can well map risk factors and their labels as a unit into a vector, and downsampling plays a crucial role in improving the training efficiency of deep CNN. What’s more, the shortcut connections with pre-activation used in our model architecture implements dimension-matching free in training.

Relation Extraction Based on Fusion Dependency Parsing from Chinese EMRs

The Electronic Medical Record (EMR) contains a great deal of medical knowledge related to patients, which has been widely used in the construction of medical knowledge graphs. Previous studies mainly focus on the features based on surface semantics of EMRs for relation extraction, such as contextual feature, but the features of sentence structure in Chinese EMRs have been neglected. In this paper, a fusion dependency parsing-based relation extraction method is proposed. Specifically, this paper extends basic features with medical record feature and indicator feature that are applicable to Chinese EMRs. Furthermore, dependency syntactic features are introduced to analyse the dependency structure of sentences. Finally, the F1 value of relation extraction based on extended features is 4.87% higher than that of relation extraction based on basic features. And compared with the former, the F1 value of relation extraction based on fusion dependency parsing is increased by 4.39%. The results of experiments performed on a Chinese EMR data set show that the extended features and dependency parsing all contribute to the relation extraction.

Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation.

BackgroundMedical entity recognition is a key technology that supports the development of smart medicine. Existing methods on English medical entity recognition have undergone great development, but their progress in the Chinese language has been slow. Because of limitations due to the complexity of the Chinese language and annotated corpora, these methods are based on simple neural networks, which cannot effectively extract the deep semantic representations of electronic medical records (EMRs) and be used on the scarce medical corpora. We thus developed a new Chinese EMR (CEMR) dataset with six types of entities and proposed a multi-level representation learning model based on Bidirectional Encoder Representation from Transformers (BERT) for Chinese medical entity recognition.ObjectiveThis study aimed to improve the performance of the language model by having it learn multi-level representation and recognize Chinese medical entities.MethodsIn this paper, the pretraining language representation model was investigated; utilizing information not only from the final layer but from intermediate layers was found to affect the performance of the Chinese medical entity recognition task. Therefore, we proposed a multi-level representation learning model for entity recognition in Chinese EMRs. Specifically, we first used the BERT language model to extract semantic representations. Then, the multi-head attention mechanism was leveraged to automatically extract deeper semantic information from each layer. Finally, semantic representations from multi-level representation extraction were utilized as the final semantic context embedding for each token and we used softmax to predict the entity tags.ResultsThe best F1 score reached by the experiment was 82.11% when using the CEMR dataset, and the F1 score when using the CCKS (China Conference on Knowledge Graph and Semantic Computing) 2018 benchmark dataset further increased to 83.18%. Various comparative experiments showed that our proposed method outperforms methods from previous work and performs as a new state-of-the-art method.ConclusionsThe multi-level representation learning model is proposed as a method to perform the Chinese EMRs entity recognition task. Experiments on two clinical datasets demonstrate the usefulness of using the multi-head attention mechanism to extract multi-level representation as part of the language model.

Improving the Named Entity Recognition of Chinese Electronic Medical Records by Combining Domain Dictionary and Rules.

Electronic medical records are an integral part of medical texts. Entity recognition of electronic medical records has triggered many studies that propose many entity extraction methods. In this paper, an entity extraction model is proposed to extract entities from Chinese Electronic Medical Records (CEMR). In the input layer of the model, we use word embedding and dictionary features embedding as input vectors, where word embedding consists of a character representation and a word representation. Then, the input vectors are fed to the bidirectional long short-term memory to capture contextual features. Finally, a conditional random field is employed to capture dependencies between neighboring tags. We performed experiments on body classification task, and the F1 values reached 90.65%. We also performed experiments on anatomic region recognition task, and the F1 values reached 93.89%. On both tasks, our model had higher performance than state-of-the-art models, such as Bi-LSTM-CRF, Bi-LSTM-Attention, and Vote. Through experiments, our model has a good effect when dealing with small frequency entities and unknown entities; with a small training dataset, our method showed 2–4% improvement on F1 value compared to the basic Bi-LSTM-CRF models. Additionally, on anatomic region recognition task, besides using our proposed entity extraction model, 12 rules we designed and domain dictionary were adopted. Then, in this task, the weighted F1 value of the three specific entities extraction reached 84.36%.

Comparing the Performance of Charlson and Elixhauser Comorbidity Indices to Predict In-Hospital Mortality Among a Chinese Population.

ObjectiveEarlier comorbidity measures have been developed or validated using the North American population. This study aims to compare five Charlson or Elixhauser comorbidity indices to predict in-hospital mortality using a large electronic medical record database from Shanxi, China.MethodsUsing the primary diagnosis code and surgery procedure codes, we identified four hospitalized patient cohorts, hospitalized between 2013 and 2017, in Shanxi, China, as follows: congestive heart failure (CHF, n=41,577), chronic renal failure (CRF, n=40,419), diabetes (n=171,355), and percutaneous coronary intervention (PCI, n=39,097). We used logistic regression models and c-statistics to evaluate the in-hospital mortality predictive performance of two multiple comorbidity indicator variables developed by Charlson in 1987 and Elixhauser in 1998 and three single numeric scores by Quan in 2011, van Walraven in 2009, and Moore 2017.ResultsElixhauser comorbidity indicator variables had consistently higher c-statistics (0.824, 0.843, 0.904, 0.853) than all other four comorbidity measures, across all four disease cohorts. Moore’s comorbidity score outperformed the other two score systems in CHF, CRF, and diabetes cohorts (c-statistics: 0.776, 0.832, 0.869), while van Walraven’s score outperformed all others among PCI patients (c-statistics: 0.827).ConclusionElixhauser comorbidity indicator variables are recommended, when applied to large Chinese electronic medical record databases, while Moore’s score system is appropriate for relatively small databases.

Research on Chinese medical named entity recognition based on collaborative cooperation of multiple neural network models.

Medical named entity recognition (NER) in Chinese electronic medical records (CEMRs) has drawn much research attention, and plays a vital prerequisite role for extracting high-value medical information. In 2018, China Health Information Processing Conference (CHIP2018) organized a medical NER academic competition aiming to extract three types of malignant tumor entity from CEMRs. Since the three types of entity are highly domain-specific and interdependency, extraction of them cannot be achieved with a single neural network model. Based on comprehensive study of the three types of entity and the entity interdependencies, we propose a collaborative cooperation of multiple neural network models based approach, which consists of two BiLSTM-CRF models and a CNN model. In order to tackle the problem that target scene dataset is small and entity distributions are sparse, we introduce non-target scene datasets and propose sentence-level neural network model transfer learning. Based on 30,000 real-world CEMRs, we pre-train medical domain-specific Chinese character embeddings with word2vec, GloVe and ELMo, and apply them to our approach respectively to validate effects of pre-trained language models in Chinese medical NER. Also, as control experiments, we apply Gated Recurrent Unit to our approach. Finally, our approach achieves an overall F1-score of 87.60%, which is the state-of-the-art performance to the best of our knowledge. In addition, our approach has won the champion of the medical NER academic competition organized by 2019 China Conference on Knowledge Graph and Semantic Computing, which proves the outstanding generalization ability of our approach.

A self-attention based neural architecture for Chinese medical named entity recognition.

The combination of medical field and big data has led to an explosive growth in the volume of electronic medical records (EMRs), in which the information contained has guiding significance for diagnosis. And how to extract these information from EMRs has become a hot research topic. In this paper, we propose an ELMo-ET-CRF model based approach to extract medical named entity from Chinese electronic medical records (CEMRs). Firstly, a domain-specific ELMo model is fine-tuned on a common ELMo model with 4679 raw CEMRs. Then we use the encoder from Transformer (ET) as our model's encoder to alleviate the long context dependency problem, and the CRF is utilized as the decoder. At last, we compare the BiLSTM-CRF and ET-CRF model with word2vec and ELMo embeddings to CEMRs respectively to validate the effectiveness of ELMo-ET-CRF model. With the same training data and test data, the ELMo-ET-CRF outperforms all the other mentioned model architectures in this paper with 85.59% F1-score, which indicates the effectiveness of the proposed model architecture, and the performance is also competitive on the CCKS2019 leaderboard.

A Natural Language Processing Pipeline of Chinese Free-Text Radiology Reports for Liver Cancer Diagnosis

Despite the rapid development of natural language processing (NLP) implementation in electronic medical records (EMRs), Chinese EMRs processing remains challenging due to the limited corpus and specific grammatical characteristics, especially for radiology reports. In this study, we designed an NLP pipeline for the direct extraction of clinically relevant features from Chinese radiology reports, which is the first key step in computer-aided radiologic diagnosis. The pipeline was comprised of named entity recognition, synonyms normalization, and relationship extraction to finally derive the radiological features composed of one or more terms. In named entity recognition, we incorporated lexicon into deep learning model bidirectional long short-term memory-conditional random field (BiLSTM-CRF), and the model finally achieved an F1 score of 93.00%. With the extracted radiological features, least absolute shrinkage and selection operator and machine learning methods (support vector machine, random forest, decision tree, and logistic regression) were used to build the classifiers for liver cancer prediction. For liver cancer diagnosis, random forest had the highest predictive performance in liver cancer diagnosis (F1 score 86.97%, precision 87.71%, and recall 86.25%). This work was a comprehensive NLP study focusing on Chinese radiology reports and the application of NLP in cancer risk prediction. The proposed NLP pipeline for the radiological feature extraction could be easily implemented in other kinds of Chinese clinical texts and other disease predictive tasks.

An Algorithm of Query Expansion for Chinese EMR Retrieval by Improving Expansion Term Weights and Retrieval Scores

Query expansion (QE) has been widely used in electronic medical record (EMR) retrieval for assisted diagnosis and clinical research. However, existing QE algorithms haven't achieved satisfactory performance in Chinese EMR retrieval, and one noticeable problem is that the weights of expansion terms and retrieval scores have unreasonable factors for lack of the solid consideration of clinical needs. Here we propose an algorithm of QE for Chinese EMR retrieval by improving expansion term weights and retrieval scores. First, the weights of expansion terms are assigned with semantic similarities, category weights and co-occurrence frequencies between expansion terms and multiple query terms. Then the retrieval scores calculated by expansion terms are limited to reduce the query drift caused by high-frequency expansion terms. Experiment results show that our method gets a 33.3% increase in the precision at top 10, a 90.4% increase in the recall, and a 13.2% increase in MAP compared with four baselines. It proves that our improvement scheme can ensure the accuracy of expansion term weights and decrease the query drift caused by QE, which substantially improves the performance of Chinese EMR retrieval.

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

BackgroundClinical 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.MethodsFrom 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.ResultsBased 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.ConclusionsOur 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.

Medical knowledge embedding based on recursive neural network for multi-disease diagnosis.

The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge, but also establish the quantifiable relationship among embeddings. In this paper, we propose a recursive neural knowledge network (RNKN), which combines medical knowledge based on first-order logic with a recursive neural network for multi-disease diagnosis. After the RNKN is efficiently trained using manually annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented knowledge embeddings and weight matrixes are learned. The experimental results confirm that the diagnostic accuracy of the RNKN is superior to those of four machine learning models, four classical neural networks and Markov logic network. The results also demonstrate that the more explicit the evidence extracted from CEMRs, the better the performance. The RNKN gradually reveals the interpretation of knowledge embeddings as the number of training epochs increases.

Named Entity Recognition in Chinese Electronic Medical Records Based on the Model of Bidirectional Long Short-Term Memory with a Conditional Random Field Layer.

Named entity recognition in electronic medical records is of great significance to the construction of medical knowledge maps. This paper proposes a model of bidirectional Long Short-Term Memory with a conditional random field layer(BiLSTM-CRF). In terms of simultaneously identifying 5 types of clinical entities from CCKS2018 Chinese EHRs corpus, the BiLSTM-CRF model finally achieved better performance than the baseline CRF model (F-score of 84.23% vs 82.49%).

Exploiting the concept level feature for enhanced name entity recognition in Chinese EMRs

The accumulation and explosive growth of the electronic medical records (EMRs) make the name entity recognition (NER) technologies become critical for the meaningful use of EMR data and then the practice of evidence-based medicine. The dominate NER approaches use the distributed representation of the words and characters to build deep learning-based NER models. However, for the task of biomedical named entity recognition, there are a large amount of complicated medical terminologies that are composed of multiple words. Splitting these terminologies to learn the word and character embeddings might cause semantic ambiguities. In this paper, we treat each medical terminology as a concept and propose a concept-enhanced named entity recognition model (CNER), where the features from three different granularities (i.e., concept, word, and character) are combined together for bio-NER. The extensive experiments are conducted on two real-world corpora: fully labeled corpus and partially labeled corpus. CNER achieves the highest F1 score (fully labeled corpus: precision = 88.23, recall = 88.29, and F1 = 88.26; partially labeled corpus: precision = 87.03, recall = 88.19, and F1 = 87.61) by outperforming the baseline CW-BLSTM-CRF approach for 0.58% and 1.15% respectively, which demonstrates the effectiveness of the proposed approach.

A hybrid approach for named entity recognition in Chinese electronic medical record

BackgroundWith the rapid spread of electronic medical records and the arrival of medical big data era, the application of natural language processing technology in biomedicine has become a hot research topic.MethodsIn this paper, firstly, BiLSTM-CRF model is applied to medical named entity recognition on Chinese electronic medical record. According to the characteristics of Chinese electronic medical records, obtain the low-dimensional word vector of each word in units of sentences. And then input the word vector to BiLSTM to realize automatic extraction of sentence features. And then CRF performs sentence-level word tagging. Secondly, attention mechanism is added between the BiLSTM and the CRF to construct Attention-BiLSTM-CRF model, which can leverage document-level information to alleviate tagging inconsistency. In addition, this paper proposes an entity auto-correct algorithm to rectify entities according to historical entity information. At last, a drug dictionary and post-processing rules are well-built to rectify entities, to further improve performance.ResultsThe final F1 scores of the BiLSTM-CRF and Attention-BiLSTM-CRF model on given test dataset are 90.15 and 90.82% respectively, both of which are higher than 89.26%, which is the best F1 score on the test dataset except ours.ConclusionOur approach can be used to recognize medical named entity on Chinese electronic medical records and achieves the state-of-the-art performance on the given test dataset.

Constructing a Chinese electronic medical record corpus for named entity recognition on resident admit notes

BackgroundElectronic Medical Records(EMRs) contain much medical information about patients. Medical named entity extracting from EMRs can provide value information to support doctors’ decision making. The research on information extraction of Chinese Electronic Medical Records is still behind that has done in English.MethodsThis paper proposed a practical annotation scheme for medical entity extraction on Resident Admit Notes (RANs), and a model which can automatic extract medical entity. Nine types of clinical entities, four types of clinical relationships were defined in our annotation scheme. An end-to-end deep neural network with convolution neural network and long-short term memory units was applied in our model for the medical named entity recognition(NER).ResultWe annotated RANs in three rounds. The overall F-score of annotation consistency was up to 97.73%. And our NER model on the 255 annotated RANs achieved the best F-score of 91.08%.ConclusionThe annotation scheme and the model for NER in this paper are effective to extract medical named entity from RANs and provide the basis for fully excavating the patient’s information.

A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records

BackgroundThe Named Entity Recognition (NER) task as a key step in the extraction of health information, has encountered many challenges in Chinese Electronic Medical Records (EMRs). Firstly, the casual use of Chinese abbreviations and doctors’ personal style may result in multiple expressions of the same entity, and we lack a common Chinese medical dictionary to perform accurate entity extraction. Secondly, the electronic medical record contains entities from a variety of categories of entities, and the length of those entities in different categories varies greatly, which increases the difficult in the extraction for the Chinese NER. Therefore, the entity boundary detection becomes the key to perform accurate entity extraction of Chinese EMRs, and we need to develop a model that supports multiple length entity recognition without relying on any medical dictionary.MethodsIn this study, we incorporate part-of-speech (POS) information into the deep learning model to improve the accuracy of Chinese entity boundary detection. In order to avoid the wrongly POS tagging of long entities, we proposed a method called reduced POS tagging that reserves the tags of general words but not of the seemingly medical entities. The model proposed in this paper, named SM-LSTM-CRF, consists of three layers: self-matching attention layer – calculating the relevance of each character to the entire sentence; LSTM (Long Short-Term Memory) layer – capturing the context feature of each character; CRF (Conditional Random Field) layer – labeling characters based on their features and transfer rules.ResultsThe experimental results at a Chinese EMRs dataset show that the F1 value of SM-LSTM-CRF is increased by 2.59% compared to that of the LSTM-CRF. After adding POS feature in the model, we get an improvement of about 7.74% at F1. The reduced POS tagging reduces the false tagging on long entities, thus increases the F1 value by 2.42% and achieves an F1 score of 80.07%.ConclusionsThe POS feature marked by the reduced POS tagging together with self-matching attention mechanism puts a stranglehold on entity boundaries and has a good performance in the recognition of clinical entities.