Dataset Information

Extracting entities with attributes in clinical text via joint deep learning.

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.

SUBMITTER: Shi X

PROVIDER: S-EPMC7647140 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Extracting entities with attributes in clinical text via joint deep learning.

Shi Xue X Yi Yingping Y Xiong Ying Y Tang Buzhou B Chen Qingcai Q Wang Xiaolong X Ji Zongcheng Z Zhang Yaoyun Y Xu Hua H

Journal of the American Medical Informatics Association : JAMIA 20191201 12

<h4>Objective</h4>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 ...[more]

PMID: 31550346

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Dataset Information

Extracting entities with attributes in clinical text via joint deep learning.

Objective

Materials and methods

Results

Conclusions

Publications

Extracting entities with attributes in clinical text via joint deep learning.

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