This paper is in the following e-collection/theme issue:

Natural Language Processing (535) Machine Learning (1121) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (103) Big Data (183) Clinical Informatics (653) Pharmacovigilance (93) Secondary Use of Clinical Data for Research and Surveillance (266) Ontologies, Classifications, and Coding (172)

Published on in Vol 9 , No 12 (2021) :December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28632, first published .
Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach

Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach

Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach

Authors of this article:

Daphne Chopard 1 Author Orcid Image ;   Matthias S Treder 1 Author Orcid Image ;   Padraig Corcoran 1 Author Orcid Image ;   Nagheen Ahmed 2 Author Orcid Image ;   Claire Johnson 2 Author Orcid Image ;   Monica Busse 2 Author Orcid Image ;   Irena Spasic 1 Author Orcid Image
Article Authors Cited by (1) Tweetations (4) Metrics

Daphne Chopard   1 , MSc ;   Matthias S Treder   1 , PhD ;   Padraig Corcoran   1 , PhD ;   Nagheen Ahmed   2 , BSc ;   Claire Johnson   2 , BSc ;   Monica Busse   2 , PhD ;   Irena Spasic   1 , PhD

1 School of Computer Science & Informatics, Cardiff University, Cardiff, United Kingdom

2 Centre for Trials Research, Cardiff University, Cardiff, United Kingdom

Corresponding Author:

  • Irena Spasic, PhD
  • School of Computer Science & Informatics
  • Cardiff University
  • Abacws
  • Senghennydd Road
  • Cardiff, CF24 4AG
  • United Kingdom
  • Phone: 44 2920870032
  • Email: spasici@cardiff.ac.uk