The current state of the art in post-marketing drug surveillance utilizes voluntarily submitted reports of suspected adverse drug reactions. We present data mining methods that transform unstructured patient notes taken by doctors, nurses and other clinicians into a de-identified, temporally ordered, patient-feature matrix using standardized medical terminologies. We demonstrate how to use the resulting high-throughput data to monitor for adverse drug events based on the clinical notes in the EHR.
|Original language||English (US)|
|Journal||AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science|
|State||Published - 2013|