Machine Learning for Health (ML4H)

Machine Learning for Health (ML4H) is an effort led by the Broad Institute in collaboration with faculty members from Massachusetts General Hospital, Brigham and Women’s Hospital, and MIT. Leveraging an interdisciplinary team of physicians, software engineers, and machine learning experts, we build open-source software tools, and develop high-impact collaborations across academia and industry. Our goal is to use machine learning to transform fundamental research into the genetic underpinnings of disease, disease sub-segmentation, and risk prediction with applications in clinical trials and clinical decision support. Although our initial array of projects are focused on cardiovascular disease, our ultimate vision is to accelerate the real-world impact of clinical ML across all areas of medicine.

Data Sciences Platform:
Puneet Batra
Paolo Di Achille
Nathaniel Diamant
Samuel Friedman
Marcus Klarqvist
Anthony Philippakis
Christopher Reeder
Gopal Sarma
Pulkit Singh

Cardiovascular Disease Initiative:
Saaket Agrawal
Chris Anderson
Seung-Hoan Choi
Jon Cunningham
Patrick Ellinor
Connor Emdin
Akl C. Fahed
Julian Haimovich
Lia Harrington
Jennifer E. Ho
Amit V. Khera
Shaan Khurshid
Emily Lau
Steve Lubitz
Pradeep Natarajan
James Pirruccello
Clay Turner

Alliance and Project Management:
Alice McElhinney
Candace Patterson
Trish Vosburg

Administrative Support:
Liz Itkowsky
Winnie Kimaiga
Emily Garcia

Collaborators:

Aaron Aguirre
Erik Reinertsen
Steven Song
Collin Stultz
Brandon Westover

Publications

James P. Pirruccello, Mark D. Chaffin, Stephen J. Fleming, Alessandro Arduini, Honghuang Lin, Shaan Khurshid, Elizabeth L. Chou, Samuel N. Friedman, Alexander G. Bick, Lu-Chen Weng, Seung Hoan Choi, Amer-Denis Akkad, Puneet Batra, Nathan R. Tucker, Amelia W. Hall, Carolina Roselli, Emelia J. Benjamin, Shamsudheen K. Vellarikkal, Rajat M. Gupta, Christian M. Stegman, Jennifer E. Ho, Udo Hoffmann, Steven A. Lubitz, Anthony Philippakis, Mark E. Lindsay, Patrick T. Ellinor. "Deep learning enables genetic analysis of the human thoracic aorta." bioRxiv (2020).

Mary E Haas, James P Pirruccello, Samuel N Friedman, Connor A Emdin, Veeral H Ajmera, Tracey G Simon, Julian R Homburger, Xiuqing Guo, Matthew Budoff, Kathleen E Corey, Alicia Y Zhou, Anthony Philippakis, Patrick T. Ellinor, Rohit Loomba, Puneet Batra, Amit V Khera. "Machine learning enables new insights into clinical significance of and genetic contributions to liver fat accumulation." medRxiv (2020).

James P. Pirruccello, Alexander Bick, Samuel Friedman, Mark Chaffin, Krishna G. Aragam, Seung Hoan Choi, Steven A. Lubitz, Carolyn Y. Ho, Kenney Ng, Anthony Philippakis, Patrick T. Ellinor, Sekar Kathiresan, Amit V. Khera. "Titin truncating variants in adults without known congestive heart failure." Journal of the American College of Cardiology 75.10 (2020): 1239-1241.

Samuel Friedman, Nathaniel Diamant, James Pirruccello, Puneet Batra, Steven Lubitz, and Anthony Philippakis. 2019. “Genome Wide Associations of Learned Low Dimensional Representations of Cardiac MRI.” In Proceedings of the NeurIPS 2019 Workshop “Learning Meaningful Representations of Life.” (2019).

Kenney Ng, Uri Kartoun, Puneet Batra, Shaan Khurshid, Amit V. Khera, Anthony Philippakis, Patrick T. Ellinor, Steven A. Lubitz. “Characterizing the Impact of Missing Data on Risk Prediction of Atrial Fibrillation from Electronic Health Records.” In Proceedings of the AMIA Symposium (2020).

James P. Pirruccello, Alexander Bick, Minxian Wang, Mark Chaffin, Samuel Friedman, Jie Yao, Xiuqing Guo, Bharath Ambale Venkatesh, Kent D. Taylor, Wendy S. Post,9, Stephen Rich, Joao A. C. Lima, Jerome I. Rotter, Anthony Philippakis, Steven A. Lubitz, Patrick T. Ellinor, Amit V. Khera, Sekar Kathiresan & Krishna G. Aragam. “Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy.” Nature Communications, 11.1, (2020): 1-10.

Gopal Sarma, Erik Reinertsen, and the ML4CVD Group. "Physiology as a Lingua Franca for Clinical Machine Learning." Patterns 1.2 (2020): 100017.