Automatic adverse drug events detection using letters to the editor

We present and test the intuition that letters to the editor in journals carry early signals of adverse drug events (ADEs). Surprisingly these letters have not yet been exploited for automatic ADE detection unlike for example, clinical records and PubMed. Part of the challenge is that it is not easy to access the full-text of letters (for the most part these do not appear in PubMed). Also letters are likely underrated in comparison with full articles. Besides demonstrating that this intuition holds we contribute techniques for post market drug surveillance. Specifically, we test an automatic approach for ADE detection from letters using off-the-shelf machine learning tools. We also involve natural language processing for feature definitions. Overall we achieve high accuracy in our experiments and our method also works well on a second new test set. Our results encourage us to further pursue this line of research.