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