An ensemble of neural models for nested adverse drug events and medication extraction with subwords.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Jan 1, 2020

Abstract

OBJECTIVE: This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2.

Authors

Meizhi Ju

College of Biomedical Engineering and Instrument Science, Zhejiang University, The Key Laboratory of Biomedical Engineering, Ministry of Education, Hangzhou, China.
Nhung T H Nguyen

University of Science, Vietnam National University, Ho Chi Minh City, 227 Nguyen Van Cu St., Ward 4, Dist. 5, Ho Chi Minh City, Viet Nam; Japan Advanced Institute of Science and Technology, 1-8 Asahidai, Nomi-shi, Ishikawa 923-1292, Japan. Electronic address: nthnhung@jaist.ac.jp.
Makoto Miwa
Sophia Ananiadou

Keywords

Drug-Related Side Effects and Adverse Reactions Electronic Health Records Humans Information Storage and Retrieval Narration Natural Language Processing Neural Networks, Computer

External Resources

View on PubMed Access via DOI PubMed (31197355)

An ensemble of neural models for nested adverse drug events and medication extraction with subwords.

Abstract

Authors

Keywords

External Resources

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