The Evolution of Clinical Decision Support Tools for the Identification and Treatment of Sepsis
Recorded On: 08/31/2023
Webinar Description:
In this sponsored webinar, Tobin Efferen, MD, MS, will explore the development of machine learning models in the prediction and diagnosis of sepsis. Dr. Efferen will review some of his own research around the inclusion of biomarkers in AI models and share information on potential future products.
No CE credits are offered for this sponsored webinar. Content was determined by the sponsor.
Webinar Sponsor:
Sepsis Alliance gratefully acknowledges the support provided by Beckman Coulter for this sponsored webinar.
Tobin Efferen, MD, MS
Medical Director, Medical and Scientific Affairs
Beckman Coulter
Tobin Efferen, MD, MS, has practiced emergency medicine on the south and west side of Chicago for the last 15 years. Initially interested in marine biology, Dr. Efferen switched gears after a brief stint at the New England Aquarium in Boston. As a laboratory technician at a biotech startup in Connecticut, he performed both basic and translation biobehavioral research on novel antipsychotic compounds. After receiving a master’s degree in Neurobehavioral Biology from NYU, he continued on to the NYU School of Medicine for his MD. He went on to complete a residency in emergency medicine at the University of Chicago. After graduating he stayed on at Mount Sinai Hospital as an attending, covering both Holy Cross Hospital and Mount Sinai Hospital in the Sinai Health System. As the Assistant Medical Director for the Emergency Department, Dr. Efferen oversaw the quality program and was the Director of the clerkship in emergency medicine for six physician assistant programs in the Chicagoland area.
He now works part time in the ED and full time as a Medical Director on the Medical/Scientific Affairs team for Beckman Coulter. His work there is focused on the scouting of novel biomarkers to improve the identification and evaluation of complex disease states such as dysregulated host response and acute kidney injury. Another area of focus is the development and implementation of machine learning-based algorithms for use in the emergent and critical care settings.