Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network

By Yonghui Wu, Min Jiang, Jianbo Lei, Hua Xu. 2015

168 citations recorded by the source corpus.

Rapid growth in electronic health records (EHRs) use has led to an unprecedented expansion of available clinical data in electronic formats. However, much of the important healthcare information is locked in the narrative documents. Therefore Natural Language Processing (NLP) technologies, e.g., Named Entity Recognition that identifies boundaries and types of entities, have been extensively studied to unlock important clinical information in free text. In this study, we investigated a novel deep learning method to recognize clinical entities in Chinese clinical documents using the minimal feature engineering approach. We developed a deep neural network (DNN) to generate word embeddings from a large unlabeled corpus through unsupervised learning and another DNN for the NER task. The experiment results showed that the DNN with word embeddings trained from the large unlabeled corpus outperformed the state-of-the-art CRF’s model in the minimal feature engineering setting, achieving the highest F1-score of 0.9280. Further analysis showed that word embeddings derived through unsupervised learning from large unlabeled corpus remarkably improved the DNN with randomized embedding, denoting the usefulness of unsupervised feature learning.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[159] applied CNNs using a set of pre-trained embeddings on clinical text for named entity recognization.”
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (2017, methodology): “[34], who apply pre-trained word embeddings on Chinese clinical text using a CNN, improving upon the CRF baselines.”
A Survey on Deep Learning for Named Entity Recognition (2018, methodology): “[116] utilized a convolutional layer to generate global features represented by a number of global hidden nodes.”
De-identification of patient notes with recurrent neural networks (2016, background): “Wu et al.(46) investigated the use of deep neural networks to learn word embeddings and perform named entity recognition of four types of clinical entities – problems, lab tests, procedures, and medications – on Chinese clinical text.”
A large language model for electronic health records (2022, background): “…concept extract are often rule-based, yet, most recent systems are based on machine learning models such as conditional random fields (CRFs) 50,51 , convolutional neural networks (CNN) 9,52 , and recurrent neural networks (RNN) implemented with long short-term memory strategy (LSTM) 10,53 .”