Background: Many factors that directly or indirectly cause adverse drug reaction (ADRs) varying from
pharmacological, immunological and genetic factors to ethnic, age, gender, social factors as well as drug and disease
related ones. On the other hand, advanced methods of statistics, machine learning and data mining allow the
users to more effectively analyze the data for descriptive and predictive purposes. The fast changes in this field
make it difficult to follow the research progress and context on ADR detection and prediction. Methods: A large
amount of articles on ADRs in the last twenty years is collected. These articles are grouped by recent data types
used to study ADRs: omics, social media and electronic medical records (EMRs), and reviewed in terms of the problem addressed, the
datasets used and methods. Results: Corresponding three tables are established providing brief information on the research for ADRs detection
and prediction. Conclusion: The data-driven approach has shown to be powerful in ADRs detection and prediction. The review
helps researchers and pharmacists to have a quick overview on the current status of ADRs detection and prediction.
Keywords: Adverse drug reaction, data-driven approach, omics data, social media data, electronic medical records.
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