Automatic detection of adverse events to predict drug label changes using text and data mining techniques

Abstract

Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks.

Application of text mining in the biomedical domain

“…Using this benchmark corpus and an ontology of adverse events, they developed a method to automatically extract adverse events from medical case reports and publicly available data sets on drug adverse effects. With these methods, a number of drug label changes for the drugs rituximab, efalizumab, and natalizumab could successfully be predicted [110,111].…”

The Proliferation of “Big Data” and Implications for Official Statistics and Statistical Agencies

“…Thanks to these types of methods, insights were possible which otherwise would have been difficult to discover. One example is the discovery of adverse effects to drugs (Gurulingappa et al, 2013;Davis et al 2013). …”

Detecting drugs and adverse events from Spanish social media streams

“…In recent years, the application of Natural Language Processing (NLP) techniques to mine adverse reactions from texts has been explored with promising results, mainly in the context of drug labels (Gurulingappa et al, 2013;Li et al, 2013;Kuhn et al, 2010), biomedical literature (Xu and Wang, 2013), medical case reports (Gurulingappa et al, 2012) and health records (Friedman, 2009;Sohn et al, 2011). However, as it will be described below, the extraction of adverse reactions from social media has received much less attention.…”