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Mahnaz Maddah

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Director, Machine Learning for Health

Broad Institute

United States

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Grant: Open

Deep Learning to Identify Digital Biomarkers of Cardiovascular Disease

Open Date: 2026-01-01

Close Date: 2026-12-31

Articles (17)

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Collaborators (11)

Michael Honigberg

Cardiologist

Massachusetts General Hospital

UNITED STATES

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James Guseh

Massachusetts General Hospital

UNITED STATES

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Sam Freesun Friedman

Senior Group Leader

Broad Institute

UNITED STATES

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Emelia Benjamin

Professor of Medicine, and Professor of Epidemiology

Boston University Medical Campus

UNITED STATES

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Pradeep Natarajan

Associate Professor

Harvard Medical School

UNITED STATES

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Bianca J. J. M. Brundel

VU medisch centrum

NETHERLANDS

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Puneet Batra

Broad Institute

UNITED STATES

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Zhi Yu

Broad Institute

UNITED STATES

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James Pirruccello

University of California, San Francisco

UNITED STATES

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Peter Libby

Harvard University

UNITED STATES

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Siddharth Swaroop

Lecturer in Artificial Intelligence

University College London

UNITED KINGDOM

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Mahnaz Maddah

Research Interests

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Recent Grants

Articles (17)

Machine learning-driven spleen imaging and genomics uncover a splenic connection to coronary artery disease

ECG‐Based Deep Learning to Predict Incident Atrial Fibrillation Burden

A Machine Learning Driven Approach to Quantifying Coronary Artery Tortuosity

Artificial Intelligence-Enhanced Electrocardiography and Health Records to Predict Cardiac Arrest

Electrocardiogram-Based Artificial Intelligence to Predict Recurrence After Cardioversion for Newly Diagnosed Atrial Fibrillation

Prediction of Pregnancy-Related Cardiovascular Outcomes Using Electrocardiogram-Based Deep Learning Estimation of Cardiorespiratory Fitness

Predicting Recurrence and Outcomes After Stressor‐Associated Atrial Fibrillation Using ECG‐Based Deep Learning

Risk-Guided Atrial Fibrillation Screening With Artificial Intelligence–Enabled Electrocardiogram Models

Using Artificial Intelligence to Adjudicate Major Adverse Cardiovascular Events in Clinical Trials

Electrocardiographic Age and Clonal Hematopoiesis of Indeterminate Potential

Natural Language Processing to Adjudicate Heart Failure Hospitalizations in Global Clinical Trials

Circulating Proteins Associated With Myocardial Interstitial Fibrosis

Contemporary Burden of Cardiovascular Disease in Pregnancy: Insights From a Real-World Pregnancy Electronic Health Record Cohort

A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogram

Associations of “Weekend Warrior” Physical Activity With Incident Disease and Cardiometabolic Health

Rare Genetic Variants in LDLR , APOB , and PCSK9 Are Associated With Aortic Stenosis

Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After Stroke

Collaborators (11)

Michael Honigberg

James Guseh

Sam Freesun Friedman

Emelia Benjamin

Pradeep Natarajan

Bianca J. J. M. Brundel

Puneet Batra

Zhi Yu

James Pirruccello

Peter Libby

Siddharth Swaroop

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