Our lab specializes in translational biomedical informatics, leveraging high-resolution electronic health record (EHR) data to develop machine learning-based decision support tools. We focus on transforming large-scale health data into actionable insights to enhance patient care, particularly by creating innovative solutions that integrate seamlessly into clinical workflows.
Ahmed Said Lab
Enhancing pediatric critical care with predictive models and decision support tools
Ahmed Said, MD, PhD
Principal investigator
Ahmed Said earned his medical degree, master’s, and PhD at Ain Shams University in Cairo, Egypt, and completed his initial pediatric residency and critical care training there. He conducted a research fellowship at WashU Medicine and returned to complete a pediatric residency and fellowship in pediatric cardiac critical care. Joining the faculty in 2015, Said is now the medical co-director of the ECMO program at St. Louis Children’s Hospital and a faculty member at AIHealth and I2DB at WashU. His research focuses on integrating EHR data to develop machine learning models for clinical decision support, including predictive models for ECMO utilization and tools for optimizing anticoagulation management in pediatric patients.
Our research projects are at the forefront of leveraging data to develop life saving predictive models in the ICU.