Xue Shi1, Yingping Yi2, Ying Xiong1, Buzhou Tang1,3, Qingcai Chen1, Xiaolong Wang1, Zongcheng Ji4, Yaoyun Zhang4, Hua Xu4. 1. Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China. 2. Department of Science and Education, The Second Affiliated Hospital of Nanchang University, Nanchang, China. 3. Peng Cheng Laboratory. 4. School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Abstract
OBJECTIVE: Extracting clinical entities and their attributes is a fundamental task of natural language processing (NLP) in the medical domain. This task is typically recognized as 2 sequential subtasks in a pipeline, clinical entity or attribute recognition followed by entity-attribute relation extraction. One problem of pipeline methods is that errors from entity recognition are unavoidably passed to relation extraction. We propose a novel joint deep learning method to recognize clinical entities or attributes and extract entity-attribute relations simultaneously. MATERIALS AND METHODS: The proposed method integrates 2 state-of-the-art methods for named entity recognition and relation extraction, namely bidirectional long short-term memory with conditional random field and bidirectional long short-term memory, into a unified framework. In this method, relation constraints between clinical entities and attributes and weights of the 2 subtasks are also considered simultaneously. We compare the method with other related methods (ie, pipeline methods and other joint deep learning methods) on an existing English corpus from SemEval-2015 and a newly developed Chinese corpus. RESULTS: Our proposed method achieves the best F1 of 74.46% on entity recognition and the best F1 of 50.21% on relation extraction on the English corpus, and 89.32% and 88.13% on the Chinese corpora, respectively, which outperform the other methods on both tasks. CONCLUSIONS: The joint deep learning-based method could improve both entity recognition and relation extraction from clinical text in both English and Chinese, indicating that the approach is promising.
OBJECTIVE: Extracting clinical entities and their attributes is a fundamental task of natural language processing (NLP) in the medical domain. This task is typically recognized as 2 sequential subtasks in a pipeline, clinical entity or attribute recognition followed by entity-attribute relation extraction. One problem of pipeline methods is that errors from entity recognition are unavoidably passed to relation extraction. We propose a novel joint deep learning method to recognize clinical entities or attributes and extract entity-attribute relations simultaneously. MATERIALS AND METHODS: The proposed method integrates 2 state-of-the-art methods for named entity recognition and relation extraction, namely bidirectional long short-term memory with conditional random field and bidirectional long short-term memory, into a unified framework. In this method, relation constraints between clinical entities and attributes and weights of the 2 subtasks are also considered simultaneously. We compare the method with other related methods (ie, pipeline methods and other joint deep learning methods) on an existing English corpus from SemEval-2015 and a newly developed Chinese corpus. RESULTS: Our proposed method achieves the best F1 of 74.46% on entity recognition and the best F1 of 50.21% on relation extraction on the English corpus, and 89.32% and 88.13% on the Chinese corpora, respectively, which outperform the other methods on both tasks. CONCLUSIONS: The joint deep learning-based method could improve both entity recognition and relation extraction from clinical text in both English and Chinese, indicating that the approach is promising.
Authors: Berry de Bruijn; Colin Cherry; Svetlana Kiritchenko; Joel Martin; Xiaodan Zhu Journal: J Am Med Inform Assoc Date: 2011-05-12 Impact factor: 4.497
Authors: Alistair E W Johnson; Tom J Pollard; Lu Shen; Li-Wei H Lehman; Mengling Feng; Mohammad Ghassemi; Benjamin Moody; Peter Szolovits; Leo Anthony Celi; Roger G Mark Journal: Sci Data Date: 2016-05-24 Impact factor: 6.444