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

New Methods (944) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (156) Pharmacovigilance (134) Methods and Instruments in Medical Informatics (126) Pharmacology and Pharmaceutics (66) Artificial Intelligence (1371)

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 (1) Tweetations (4) Metrics

Qi-Xuan Yue   1, 2 * , ME ;   Ruo-Fan Ding   1 * , PhD ;   Wei-Hao Chen   1, 2 , MS ;   Lv-Ying Wu   1 , MS ;   Ke Liu   1 , PhD ;   Zhi-Liang Ji   1, 2, 3 , PhD

1 State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China

2 National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China

3 Fujian Provincial Key Laboratory of Chemical Biology, Xiamen University, Xiamen, China

*these authors contributed equally

Corresponding Author:

  • Zhi-Liang Ji, PhD
  • State Key Laboratory of Cellular Stress Biology, School of Life Sciences
  • Faculty of Medicine and Life Sciences
  • Xiamen University
  • Number 4221 Xiang'an South Road
  • Xiang'an District
  • Xiamen, 361102
  • China
  • Phone: 86 0592-2182897
  • Email: appo@xmu.edu.cn