Role of Text Mining in Early Identification of Potential Drug Safety Issues

  • Mei Liu
  • Yong Hu
  • Buzhou Tang
Part of the Methods in Molecular Biology book series (MIMB, volume 1159)


Drugs are an important part of today’s medicine, designed to treat, control, and prevent diseases; however, besides their therapeutic effects, drugs may also cause adverse effects that range from cosmetic to severe morbidity and mortality. To identify these potential drug safety issues early, surveillance must be conducted for each drug throughout its life cycle, from drug development to different phases of clinical trials, and continued after market approval. A major aim of pharmacovigilance is to identify the potential drug–event associations that may be novel in nature, severity, and/or frequency. Currently, the state-of-the-art approach for signal detection is through automated procedures by analyzing vast quantities of data for clinical knowledge. There exists a variety of resources for the task, and many of them are textual data that require text analytics and natural language processing to derive high-quality information. This chapter focuses on the utilization of text mining techniques in identifying potential safety issues of drugs from textual sources such as biomedical literature, consumer posts in social media, and narrative electronic medical records.

Key words

Text mining Data mining Natural language processing Biomedical literature mining Pharmacovigilance Drug safety surveillance Adverse drug reaction Drug–drug interactions 



Mei Liu is supported by funds from the New Jersey Institute of Technology. Yong Hu is supported by the National Science Foundation of China (71271061, 70801020); Science and Technology Planning Project of Guangdong Province, China (2010B010600034, 2012B091100192); and Business Intelligence Key Team of Guangdong University of Foreign Studies (TD1202). Buzhou Tang is supported by the China Postdoctoral Science Foundation (2011 M500669).


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceNew Jersey Institute of Technology, University HeightsNewarkUSA
  2. 2.Institute of Business Intelligence, Guangdong University of Foreign Studies, Sun Yat-sen UniversityGuangzhouPeople’s Republic of China
  3. 3.Department of Computer Science and TechnologyHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenPeople’s Republic of China

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