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

New Methods (1134) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (215) Pharmacovigilance (171) Methods and Instruments in Medical Informatics (189) Pharmacology and Pharmaceutics (111) Artificial Intelligence (2129)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48572, first published .
Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study

Authors of this article:

Qi-Xuan Yue1, 2 Author Orcid Image ;   Ruo-Fan Ding1 Author Orcid Image ;   Wei-Hao Chen1, 2 Author Orcid Image ;   Lv-Ying Wu1 Author Orcid Image ;   Ke Liu1 Author Orcid Image ;   Zhi-Liang Ji1, 2, 3 Author Orcid Image
Article Authors Cited by (4) Tweetations (4) Metrics

Journals

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  2. Hornung E, Faisal Z, Técsi L, Lovász A, Dóczi T, Botz L. Optimising electronic documentation of medication in Hungary: Itemised, complete, historical, and standardised event recording. European Journal of Pharmaceutical Sciences 2025;209:107079 View
  3. Li B, Qi Y, Li B, Li X. Predicting adverse drug reactions for combination pharmacotherapy with cross-scale associative learning via attention modules. Nature Computational Science 2025;5(7):547 View
  4. Yang J, Sun Y, Li F, Wei Q, Wei J, Zhang Y. Evaluating the effectiveness of AI-enhanced “One Body, Two Wings” pharmacovigilance models in China: a nationwide survey on medication safety and risk management. Frontiers in Health Services 2025;5 View