Andrew Beam

Andrew Beam, PhD

Assistant Professor of Epidemiology, Harvard T.H. Chan School of Public Health
Assistant Professor of Biomedical Informatics, Harvard Medical School (Secondary)

Andrew Beam 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 Generate Biosciences, Inc., 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.

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.

The false hope of current approaches to explainable artificial intelligence in health care.
Authors: Ghassemi M, Oakden-Rayner L, Beam AL.
Lancet Digit Health
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Sharpening the resolution on data matters: a brief roadmap for understanding deep learning for medical data.
Authors: Schmaltz A, Beam AL.
Spine J
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Medication utilization in children born preterm in the first two years of life.
Authors: Levin JC, Beam AL, Fox KP, Mandl KD.
J Perinatol
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Specificity of International Classification of Diseases codes for bronchopulmonary dysplasia: an investigation using electronic health record data and a large insurance database.
Authors: Beam KS, Lee M, Hirst K, Beam A, Parad RB.
J Perinatol
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Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?
Authors: Beaulieu-Jones BK, Yuan W, Brat GA, Beam AL, Weber G, Ruffin M, Kohane IS.
NPJ Digit Med
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Second opinion needed: communicating uncertainty in medical machine learning.
Authors: Kompa B, Snoek J, Beam AL.
NPJ Digit Med
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Time to reality check the promises of machine learning-powered precision medicine.
Authors: Wilkinson J, Arnold KF, Murray EJ, van Smeden M, Carr K, Sippy R, de Kamps M, Beam A, Konigorski S, Lippert C, Gilthorpe MS, Tennant PWG.
Lancet Digit Health
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Machine Learning in Clinical Journals: Moving From Inscrutable to Informative.
Authors: Singh K, Beam AL, Nallamothu BK.
Circ Cardiovasc Qual Outcomes
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Machine learning on drug-specific data to predict small molecule teratogenicity.
Authors: Challa AP, Beam AL, Shen M, Peryea T, Lavieri RR, Lippmann ES, Aronoff DM.
Reprod Toxicol
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Estimates of healthcare spending for preterm and low-birthweight infants in a commercially insured population: 2008-2016.
Authors: Beam AL, Fried I, Palmer N, Agniel D, Brat G, Fox K, Kohane I, Sinaiko A, Zupancic JAF, Armstrong J.
J Perinatol
View full abstract on Pubmed