Machine Learning Methods to Predict Adverse Drug Events (ADEs) for Understudied Population

Title: Protect the Women & Children: Machine Learning Methods to Predict Adverse Drug Events (ADEs) for Understudied Populations Description: This talk by Dr. Nicholas Tatonetti explores using machine learning to predict adverse drug events (ADEs) in women and children. Leveraging FDA Adverse Event Reporting System (FAERS) data, Dr. Tatonetti and their lab identify sex-linked and developmental stage-specific ADEs using AI and statistical methods. Their approach generates and validates hypotheses with molecular data to improve drug safety for these vulnerable populations. Speaker: Nicholas Tatonetti, PhD, FACMI Professor of Computational Biomedicine Vice Chair for Operations, Department of Computational Biomedicine Associate Director for Computational Oncology, Cedars-Sinai Cancer Date: June 18, 2024 The University of Washington is committed to ensuring digital accessibility in our services, programs, and activities. If you encounter accessibility barriers using videos found on this channel, please contact UW Video at uwvideo [at] uw [dot] edu.