Chinese Clinical Named Entity Recognition Using Residual Dilated Convolutional Neural Network With Conditional Random Field

Qiu, Jiahui; Zhou, Yangming; Wang, Qi; Ruan, Tong; Gao, Ju

doi:10.1109/tnb.2019.2908678

Cited by 59 publications

(22 citation statements)

References 29 publications

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“…Based on NER, we experimentally compare our focused attention model with other reference algorithms. These algorithms consist of two NER models in medical domain (i.e., Bi-LSTM [20] and RDCNN [21]) and one joint model in generic domain (i.e., Joint-Bi-LSTM [4]). In addition, we originally plan to use the joint model [16] in the medical domain, but the character-level representations cannot be implemented in Chinese.…”

Section: Resultsmentioning

confidence: 99%

Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

Kui

Zhou

et al. 2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Entity and relation extraction is the necessary step in structuring medical text. However, the feature extraction ability of the bidirectional long short term memory network in the existing model does not achieve the best effect. At the same time, the language model has achieved excellent results in more and more natural language processing tasks. In this paper, we present a focused attention model for the joint entity and relation extraction task. Our model integrates well-known BERT language model into joint learning through dynamic range attention mechanism, thus improving the feature representation ability of shared parameter layer. Experimental results on coronary angiography texts collected from Shuguang Hospital show that the F1-scores of named entity recognition and relation classification tasks reach 96.89% and 88.51%, which outperform state-of-the-art methods by 1.65% and 1.22%, respectively.

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Section: Resultsmentioning

confidence: 99%

Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

Kui

Zhou

et al. 2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…The BiLSTM-CRF model has outperformed the traditional models and achieved the state-of-art results in Chinese NER tasks. Some studied introduced dictionaries into deep neural networks and got higher performance than the reference model [18,47]. Chinese and English clinical texts have different characteristics in linguistic traits and writing styles.…”

Section: Discussionmentioning

confidence: 99%

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

Liu

Zhang

et al. 2020

IEEE Access

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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.

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“…The second step is to pick out lab indicators related to special disease and construct the special disease lab indicator KB. The last step is to named entity recognize [ 32 , 33 ] using the special disease indicator KB and turn text descriptions into structured data and map synonymous names into standard names.…”

Section: Discussionmentioning

confidence: 99%

Lab indicators standardization method for the regional healthcare platform: a case study on heart failure

Liang

Zhang

et al. 2020

BMC Med Inform Decis Mak

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Background Laboratory indicator test results in electronic health records have been applied to many clinical big data analysis. However, it is quite common that the same laboratory examination item (i.e., lab indicator) is presented using different names in Chinese due to the translation problem and the habit problem of various hospitals, which results in distortion of analysis results. Methods A framework with a recall model and a binary classification model is proposed, which could reduce the alignment scale and improve the accuracy of lab indicator normalization. To reduce alignment scale, tf-idf is used for candidate selection. To assure the accuracy of output, we utilize enhanced sequential inference model for binary classification. And active learning is applied with a selection strategy which is proposed for reducing annotation cost. Results Since our indicator standardization method mainly focuses on Chinese indicator inconsistency, we perform our experiment on Shanghai Hospital Development Center and select clinical data from 8 hospitals. The method achieves a F1-score 92.08$$\%$$ % in our final binary classification. As for active learning, the new strategy proposed performs better than random baseline and could outperform the result trained on full data with only 43$$\%$$ % training data. A case study on heart failure clinic analysis conducted on the sub-dataset collected from SHDC shows that our proposed method is practical in the application with good performance. Conclusion This work demonstrates that the structure we proposed can be effectively applied to lab indicator normalization. And active learning is also suitable for this task for cost reduction. Such a method is also valuable in data cleaning, data mining, text extracting and entity alignment.

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Chinese Clinical Named Entity Recognition Using Residual Dilated Convolutional Neural Network With Conditional Random Field

Cited by 59 publications

References 29 publications

Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

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

Lab indicators standardization method for the regional healthcare platform: a case study on heart failure

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