Utilizing Deep Learning for Detecting Adverse Drug Events in Structured and Unstructured Regulatory Drug Data Sets.

This work successfully demonstrated a proof-of-concept machine learning approach to automatically detect AEs in several textual regulatory data sets to support post-market regulatory activities. Limited instances of each AE class likely prohibited models from generalizing data effectively. Additional data may permit more robust validation.