Named Entity Recognition From Biomedical Texts Using a Fusion Attention-Based BiLSTM-CRF

Wei, Hao; Gao, Mingyuan; Zhou, Ai; Chen, Fei; Wen, Quan; Wang, Chunli; Lu, Mingyu

doi:10.1109/access.2019.2920734

Cited by 38 publications

(21 citation statements)

References 39 publications

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“…They mainly relied on manual features [8], [30]. Neural network approaches, especially for the LSTM-CRF model [15], [16], [31], [34], can significantly improve the performance of the named entity recognition task in the medical field. But these methods are applied to an entire sentence.…”

Section: Related Workmentioning

confidence: 99%

Information Extraction for Intestinal Cancer Electronic Medical Records

et al. 2020

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The data generated by the structured electronic medical records is helpful for mining and extracting medical data, and it is an effective way to make effective use of valuable data resources. However, the hospitals have accumulated a large number of unstructured data in electronic medical records, which cannot be effectively searched, resulting in serious waste of resources. In this paper, we study the problem of extracting attribute values from the unstructured text in electronic medical records. By observing intestinal cancer diagnostic texts, our attributes have two categories-discriminative attributes and extractive attributes, which use the text classification and the sequence labeling to tackle attribute values extraction problems. For discriminative attributes, we firstly divide the text into sentences/segments as instances. Secondly, we finetune the pre-trained word embedding to capture domain-specific semantics/knowledge. Thirdly, we also use an attention mechanism to select the most important instance for different attribute extractors. Finally, multi-tasking learning is used to share useful information to get better experimental results. For extractive attributes, we propose a novel model to get attribute values, including the BiLSTM layer, the CNN layer and the CRF layer. In particular, we use BiLSTM and CNN to learn text features and CRF as the last layer of the model. Experiments have shown that our method is superior to several competitive baseline methods.

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Section: Related Workmentioning

confidence: 99%

Information Extraction for Intestinal Cancer Electronic Medical Records

et al. 2020

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“…The best F1 score on corpus clinical notes is 86.11%. To pay attention to the significant areas when capturing features, Wei et al [12] proposed an attention-based BILSTM-CRF model, and their model obtained an F1-score of 73.50% on JNLPBA corpus.…”

Section: Related Workmentioning

confidence: 99%

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

Gao

Wang

Wang³

et al. 2020

IJERPH

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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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“…Although not used in our approach, it should be noted that emerging deep learning has become popular in the biomedical domain with neural network based methods being used to enhance text mining techniques [12,22].…”

Section: Information Retrieval In Biomedical Domainmentioning

confidence: 99%