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
