Geoffrey H Tison

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University of California, San Francisco

United States

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Recent Grants

Grant: Close

Developing a platform for deep phenotyping of heart failure with preserved ejection fraction using raw, widely-available, multi-modality data and artificial intelligence algorithms

Open Date: 2022-09-13

Close Date: 2023-08-31

Grant: Close

Racial/Ethnic Disparities in Heart Failure: A Cross Cohort Collaboration

Open Date: 2021-04-01

Close Date: 2025-03-31

Grant: Close

Prospective sudden cardiac death risk stratification using CMR and echocardiography machine learning in mitral valve prolapse

Open Date: 2020-05-26

Close Date: 2025-04-30

Grant: Close

Dynamic prediction of heart failure using real-time functional status and EHR data in the ambulatory setting

Open Date: 2018-01-15

Close Date: 2022-11-30

Articles (14)

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2024

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2022

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2022

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2022

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Guillaume Marquis-Gravel

Université de Montréal

CANADA

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Geoffrey H Tison

Research Interests

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Contact this professor

Recent Grants

Articles (14)

Automated Assessment of Right Atrial Pressure From Ultrasound Videos Using Machine Learning

Evaluation of stenoses using AI video models applied to coronary angiography

Artificial Intelligence to Complement, Not Replace, Clinical Knowledge

Automated Assessment of Cardiac Systolic Function From Coronary Angiograms With Video-Based Artificial Intelligence Algorithms.

Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury.

Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning.

Interindividual Variability in Self-Monitoring of Blood Pressure Using Consumer-Purchased Wireless Devices

Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective

Worldwide physical activity trends since COVID-19 onset.

Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease

Assessment of Disease Status and Treatment Response With Artificial Intelligence-Enhanced Electrocardiography in Obstructive Hypertrophic Cardiomyopathy.

Using machine learning to uncover heterogeneity of beta blocker response in heart failure.

Performance of a Convolutional Neural Network and Explainability Technique for 12-Lead Electrocardiogram Interpretation.

Predictors of incident SARS-CoV-2 infections in an international prospective cohort study.

Collaborators (6)

Joseph Gonzalez

Robert Avram

Joshua Barrios

Lora Burke

Yaguang Zheng

Guillaume Marquis-Gravel

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