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Adverse drug reaction (ADR) is widely concerned for public health issue. ADRsare one of most common causes to withdraw some drugs from market. Prescriptionevent monitoring (PEM) is an important approach to detect the adverse drugreactions. The main problem to deal with this method is how to automaticallyextract the medical events or side effects from high-throughput medical events,which are collected from day to day clinical practice. In this study we proposea novel concept of feature matrix to detect the ADRs. Feature matrix, which isextracted from big medical data from The Health Improvement Network (THIN)database, is created to characterize the medical events for the patients whotake drugs. Feature matrix builds the foundation for the irregular and bigmedical data. Then feature selection methods are performed on feature matrix todetect the significant features. Finally the ADRs can be located based on thesignificant features. The experiments are carried out on three drugs:Atorvastatin, Alendronate, and Metoclopramide. Major side effects for each drugare detected and better performance is achieved compared to other computerizedmethods. The detected ADRs are based on computerized methods, furtherinvestigation is needed.

Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database