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. Our goal is to use machine learning to drive new fundamental research into the genetic underpinnings of disease, disease subgroup classification, and risk prediction with applications in clinical trials and clinical decision support.
Our initial array of projects are focused on cardiovascular disease and healthy aging. Our ultimate vision is to accelerate the real-world impact of clinical ML across all areas of medicine.
The Community Care Cohort Project ("C3PO"), led by Shaan Khurshid, has longitudinal high-resolution clinical data for over a half-million individuals. ML4H uses machine learning on C3PO to drive new biological discovery and clinical impact.
First author James Pirruccello used deep learning to characterize aortic dimensions across 37K UK Biobank participants, discovering ~100 loci in a Genome Wide Association Study.
ML4H collaborates with industry partners, including IBM and Bayer Pharmaceutical, to use ML, genomics, and clinical data to predict clinical disease.
We use deep learning to build multimodal representations of cardiac function that can translate across modalities (ECGs and MRIs shown here).
ML4H also maintains the open-source ML4H codebase on behalf of the entire research community.
To discuss collaboration opportunities, please contact ML4H's alliance manager, Alice McElhinney.
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