Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF

• Published in: BMC medical informatics and decision making 2019-04, Vol.19 (Suppl 3), p.74-74, Article 74
• Main Authors: Tang, Buzhou, Wang, Xiaolong, Yan, Jun, Chen, Qingcai
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Benchmarking; Character recognition; China; Chinese clinical entity recognition; Conditional random field; Convolutional neural network; Datasets; Datasets as Topic; Electronic Health Records; Electronic records; Humans; Informatics; International conferences; Long-short term memory; Medical informatics; Medical records; Medical research; Natural Language Processing; Neural network; Neural networks; Neural Networks, Computer; Novels; Recognition; Semantics; Short-term memory; Technology application
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-019-0787-y

Clinical entity recognition as a fundamental task of clinical text processing has been attracted a great deal of attention during the last decade. However, most studies focus on clinical text in English rather than other languages. Recently, a few researchers have began to study entity recognition in Chinese clinical text. In this paper, a novel deep neural network, called attention-based CNN-LSTM-CRF, is proposed to recognize entities in Chinese clinical text. Attention-based CNN-LSTM-CRF is an extension of LSTM-CRF by introducing a CNN (convolutional neural network) layer after the input layer to capture local context information of words of interest and an attention layer before the CRF layer to select relevant words in the same sentence. In order to evaluate the proposed method, we compare it with other two currently popular methods, CRF (conditional random field) and LSTM-CRF, on two benchmark datasets. One of the datasets is publically available and only contains contiguous clinical entities, and the other one is constructed by us and contains contiguous and discontiguous clinical entities. Experimental results show that attention-based CNN-LSTM-CRF outperforms CRF and LSTM-CRF. CNN and attention mechanism are individually beneficial to LSTM-CRF-based Chinese clinical entity recognition system, no matter whether contiguous clinical entities are considered. The conribution of attention mechanism is greater than CNN.