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.

Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data.
Authors: Beam AL, Kompa B, Schmaltz A, Fried I, Weber G, Palmer N, Shi X, Cai T, Kohane IS.
Pac Symp Biocomput
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Automated grouping of medical codes via multiview banded spectral clustering.
Authors: Zhang L, Zhang Y, Cai T, Ahuja Y, He Z, Ho YL, Beam A, Cho K, Carroll R, Denny J, Kohane I, Liao K, Cai T.
J Biomed Inform
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Factors associated with clinical inertia in type 2 diabetes mellitus patients treated with metformin monotherapy.
Authors: Kartoun U, Iglay K, Shankar RR, Beam A, Radican L, Chatterjee A, Pai JK, Shaw S.
Curr Med Res Opin
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Trends and Focus of Machine Learning Applications for Health Research.
Authors: Beaulieu-Jones B, Finlayson SG, Chivers C, Chen I, McDermott M, Kandola J, Dalca AV, Beam A, Fiterau M, Naumann T.
JAMA Netw Open
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Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.
Authors: Chen ML, Doddi A, Royer J, Freschi L, Schito M, Ezewudo M, Kohane IS, Beam A, Farhat M.
EBioMedicine
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Adversarial attacks on medical machine learning.
Authors: Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam AL, Kohane IS.
Science
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Feature extraction for phenotyping from semantic and knowledge resources.
Authors: Ning W, Chan S, Beam A, Yu M, Geva A, Liao K, Mullen M, Mandl KD, Kohane I, Cai T, Yu S.
J Biomed Inform
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Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes.
Authors: Beaulieu-Jones BK, Kohane IS, Beam AL.
Pac Symp Biocomput
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Concordance between gene expression in peripheral whole blood and colonic tissue in children with inflammatory bowel disease.
Authors: Palmer NP, Silvester JA, Lee JJ, Beam AL, Fried I, Valtchinov VI, Rahimov F, Kong SW, Ghodoussipour S, Hood HC, Bousvaros A, Grand RJ, Kunkel LM, Kohane IS.
PLoS One
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Artificial intelligence in healthcare.
Authors: Yu KH, Beam AL, Kohane IS.
Nat Biomed Eng
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