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.
- DOI
- 10.2196/49643
- Published
- 2024 Apr 3
- Container
- JMIR medical informatics
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.2196/49643,
title = {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.},
author = {Choo SM and Sartori D and Lee SC and Yang HC and Syed-Abdul S},
year = {2024},
journal = {JMIR medical informatics},
doi = {10.2196/49643},
url = {https://doi.org/10.2196/49643}
}RIS
TY - JOUR TI - 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. AU - Choo SM AU - Sartori D AU - Lee SC AU - Yang HC AU - Syed-Abdul S PY - 2024 JO - JMIR medical informatics DO - 10.2196/49643 UR - https://doi.org/10.2196/49643 ER -
APA
SM, C., D, S., SC, L., HC, Y., & S, S. (2024). 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.. JMIR medical informatics. https://doi.org/10.2196/49643
Source records
- pubmed · retrieved 2026-09-26T13:07:22.223Z