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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.

Sim Mei ChooDaniele SartoriSing Chet LeeHsuan-Chia YangShabbir Syed Abdul
Published in: JMIR medical informatics (2024)
Through interpretable machine learning and time-series analyses, this study identified key features that positively or negatively influence the completeness of Malaysian AE reports and unveiled how Malaysia has developed its pharmacovigilance capacity via multifaceted strategies and interventions. These findings will guide future work in enhancing pharmacovigilance and public health.
Keyphrases
  • adverse drug
  • machine learning
  • public health
  • artificial intelligence
  • electronic health record
  • drug induced
  • big data
  • emergency department
  • deep learning
  • bioinformatics analysis