Text Mining for Adverse Drug Events the Promise

Authors: Rave Harpaz Alison Callahan Suzanne Tamang Yen Low David Odgers Sam Finlayson Kenneth Jung Paea LePendu Nigam H Shah

Publish Date: 2014/08/24

Volume: 37, Issue: 10, Pages: 777-790

PDF Link

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 ADErelated 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 pharmacovigilanceNigam H Shah is a Science Advisor to Apixio Inc wwwapixiocom and Kyron Inc wwwkyroncom Rave Harpaz is an employee of Oracle Health Sciences Rave Harpaz Alison Callahan Suzanne Tamang Yen Low David Odgers Sam Finlayson Kenneth Jung Paea LePendu and Nigam H Shah have no other conflicts of interest that are directly relevant to the content of this article

Keywords: