A deep learning based named entity recognition approach for adverse drug events identification and extraction in health social media

Long Xia, G. Alan Wang, Weiguo Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Drug safety surveillance plays a significant role in supporting medication decision-making by both healthcare providers and patients. Extracting adverse drug events (ADEs) from social media provides a promising direction to addressing this challenging task. Prior studies typically perform lexicon-based extraction using existing dictionaries or medical lexicons. While those approaches can capture ADEs and identify risky drugs from patient social media postings, they often fail to detect those ADEs whose descriptive words do not exist in medical lexicons and dictionaries. In addition, their performance is inferior when ADE related social media content is expressed in an ambiguous manner. In this research, we propose a research framework using advanced natural language processing and deep learning for high-performance ADE extraction. The framework consists of training the word embeddings using a large medical domain corpus to capture precise semantic and syntactic word relationships, and a deep learning based named entity recognition method for drug and ADE entity identification and prediction. Experimental results show that our framework significantly outperforms existing models when extracting ADEs from social media in different test beds.

Original languageEnglish (US)
Title of host publicationSmart Health - International Conference, ICSH 2017, Proceedings
EditorsElena Karahanna, Indranil Bardhan, Hsinchun Chen, Daniel Dajun Zeng
Number of pages12
ISBN (Print)9783319679631
StatePublished - 2017
Externally publishedYes
EventInternational Conference on Smart Health, ICSH 2017 - Hong Kong, Hong Kong
Duration: Jun 26 2017Jun 27 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10347 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Smart Health, ICSH 2017
Country/TerritoryHong Kong
CityHong Kong


  • Adverse drug events
  • Deep learning
  • Named entity extraction
  • Pharmacovigilance
  • Social media
  • Word embeddings

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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