Andrew Beam (Harvard T. H. Chan School of Public Health) - Talk postponed, stay tuned for a new date!

Date: 

Monday, December 12, 2022, 11:00am to 12:00pm

Location: 

In person only, register here: https://forms.gle/d4cvu4wA3RLFDjeu5

Talk title: Neonatal Medicine and Machine Learning: Big Opportunities for Small Patients


Abstract: Infants born prematurely (i.e., before 37 weeks’ gestation) who are admitted to neonatal intensive care unit (NICU) experience some of the highest levels of morbidity and mortality of any pediatric population. Now, with the availability of large sources of healthcare data from insurance claims databases and electronic health records, there is an opportunity to better understand this vulnerable population and improve outcomes using data-driven approaches. In this talk, I will outline opportunities for machine learning in perinatal and neonatal medicine. I will provide an overview of the core challenges in this area and present some recent work using machine learning and causal inference to address these challenges. Finally, I will conclude with an overview of promising future directions where machine learning researchers may contribute to this important medical domain.

Andrew Beam, PhD is an assistant professor in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health, with secondary appointments in the Department of Biomedical Informatics at Harvard Medical School and the Department of Newborn Medicine at Brigham and Women’s Hospital. His research develops and applies machine-learning methods to extract meaningful insights from clinical and biological datasets, and he is the recipient of a Pioneer Award from the Robert Wood Johnson Foundation for his work on medical artificial intelligence.

Previously he was a Senior Fellow at Flagship Pioneering and the founding head of machine learning at VL56, a Flagship-backed venture that seeks to use machine learning to improve our ability to engineer proteins.

He earned his PhD in 2014 from N.C. State University for work on Bayesian neural networks, and he holds degrees in computer science (BS), computer engineering (BS), electrical engineering (BS), and statistics (MS), also from N.C. State. He completed a postdoctoral fellowship in Biomedical Informatics at Harvard Medical School and then served as a junior faculty member.

Dr. Beam’s group is principally concerned with improving, stream-lining, and automating decision-making in healthcare through the use of quantitative, data-driven methods. He does this through rigorous methodological research coupled with deep partnerships with physicians and other members of the healthcare workforce. As part of this vision, he works to see these ideas translated into decision-making tools that doctors can use to better care for their patients.

For more information, please see his group’s website at beamlab.org