RT Journal Article SR Electronic T1 Data science identifies novel drug interactions that prolong the QT interval JF bioRxiv FD Cold Spring Harbor Laboratory SP 024745 DO 10.1101/024745 A1 Tal Lorberbaum A1 Kevin J. Sampson A1 Raymond L. Woosley A1 Robert S. Kass A1 Nicholas P. Tatonetti YR 2015 UL http://biorxiv.org/content/early/2015/08/16/024745.abstract AB Drug-induced prolongation of the QT interval on the electrocardiogram (long QT syndrome, LQTS) can lead to a potentially fatal ventricular arrhythmia called torsades de pointes (TdP). 180 drugs with both cardiac and non-cardiac indications have been found to increase risk for TdP, but drug-drug interactions contributing to LQTS (QT-DDIs) remain poorly characterized. Traditional methods for mining observational healthcare data are poorly equipped to detect QT-DDI signals due to low reporting numbers and a lack of direct evidence for LQTS. In this study we present an integrative data science pipeline that addresses these limitations by identifying latent signals for QT-DDIs in the FDA’s Adverse Event Reporting System and retrospectively validating these predictions using electrocardiogram data in electronic health records. We present 26 novel QT-DDIs flagged using this method that warrant further investigation.