Data-driven Approach to Detect and Predict Adverse Drug Reactions

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.