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.

S. Agarwal (Khera Lab) and MDR Klarqvist (ML4H) built a CNN to quantify fat depots from MRI and showed that the precise variation of fat deposits across the body can modify the effect of BMI, and can be protective or harmful for developing Diabetes or Coronary Artery Disease.

The Community Care Cohort Project ("C3PO") led by Shaan Khurshid (Lubitz Lab) and Chris Reeder (ML4H) has longitudinal high-resolution clinical data for over a half-million individuals. ML4H uses NLP on the ~80B tokens in C3PO to scale phenotype ascertainment, and to drive new biological discovery and clinical impact across a wide variety of clinical analyses.

Nate Diamant (Stultz Lab, ML4H) devised a new approach for transfer learning on ECGs Patient Contrastive Learning Representations (PCLR) that creates a more performant, efficient, and pragmatic representation of an ECG that outperforms training a deep learning model from scratch in data sets with less than a few thousand labeled events.

James Pirruccello (MGH, Broad, ML4H) built a deep learning to characterize aortic dimensions across 37K UK Biobank participants, discovering ~100 loci in a Genome Wide Association Study and predicting risk of aortic dissection.