Adverse Drug Reaction Detection Using Data Mining Techniques: A Review Article

Adverse drug reactions (ADRs) are side effects that occur when taking the drug in natural doses. ADRs are a public health issue because they hospitalize millions of patients worldwide each year. Early detection of ADRs reduces economic costs and prevents fatality.Diagnosis of ADRs usually depended on voluntary reporting or medical information. But in recent years, the data sent by the user on social media has become a significant source for detecting ADR. Twitter is a social media where people use short messages as a way to communicate. Limit the number of words on Twitter allows users to use words purposefully and focused. The information provided by users about drugs and their adverse reactions on Twitter is an important resource for post-marketing drug monitoring.In recent years, machine learning and data mining methods have been considered in the field of data science for ADR detection. Important challenges in this area are divided into three parts: data pre-processing, extracting meaningful features, and selecting the best model for classification.The aim of this study is to study, review and challenge the methods of ADR diagnosis by data mining on social media, especially Twitter.