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

Adverse Drug Events Detection, Pharmacovigilance and Surveillance (204) Advanced Data Analytics in eHealth (189) eHealth Infrastructures (415) Machine Learning (1934) Quality Improvement (285) Development and Evaluation of Research Methods, Instruments and Tools (691) Public (e)Health, Digital Epidemiology and Public Health Informatics (940) Artificial Intelligence (1964)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49643, first published .
Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST

Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST

Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST

Authors of this article:

Sim Mei Choo1, 2 Author Orcid Image ;   Daniele Sartori3 Author Orcid Image ;   Sing Chet Lee1 Author Orcid Image ;   Hsuan-Chia Yang2, 4, 5, 6 Author Orcid Image ;   Shabbir Syed-Abdul2, 4, 7 Author Orcid Image
Article Authors Cited by (3) Tweetations (6) Metrics

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  3. Barbieri M, Battini V, Carnovale C, Cocco M, Papoutsi D, Heckmann N, Dong G, Rossi A, Peker S, Van Manen R, Thapar S, Sessa M. Artificial intelligence in pharmacovigilance signal management: a review of tools, implementations, research, and regulatory landscape. Expert Opinion on Drug Safety 2025:1 View