@article{5300,
  author = {P. LePendu and S. Iyer V and A. Bauer-Mehren and R. Harpaz and J. M. Mortensen and T. Podchiyska and T. A. Ferris and N. H. Shah},
  title = {Pharmacovigilance using clinical notes.},
  abstract = {<p>With increasing adoption of electronic health records (EHRs), there is an opportunity to use the free-text portion of EHRs for pharmacovigilance. We present novel methods that annotate the unstructured clinical notes and transform them into a deidentified patient-feature matrix encoded using medical terminologies. We demonstrate the use of the resulting high-throughput data for detecting drug-adverse event associations and adverse events associated with drug-drug interactions. We show that these methods flag adverse events early (in most cases before an official alert), allow filtering of spurious signals by adjusting for potential confounding, and compile prevalence information. We argue that analyzing large volumes of free-text clinical notes enables drug safety surveillance using a yet untapped data source. Such data mining can be used for hypothesis generation and for rapid analysis of suspected adverse event risk.</p>},
  year = {2013},
  journal = {Clin Pharmacol Ther},
  volume = {93},
  pages = {547-55},
  month = {06/2013},
  issn = {1532-6535},
  doi = {10.1038/clpt.2013.47},
  language = {eng},
}