Abstract Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event (ADE) detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources-such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs-that are amenable to text mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance. Text mining is needed in order to leverage several textual data sources that have the potential to improve pharmacovigilance Despite the challenges associated with processing free-text, a large body of research has demonstrated that with existing tools it is possible to extract useful safety-related information from these textual sources
Harpaz et al. prepared an overview of the state of the art in text mining for Adverse Drug Events (ADEs) REF in various contexts, such as the biomedical literature, product labelling, social media and web search logs.
Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art
Drug Safety
Drug Safety
Medicine