EMILY MACKAY

Assistant Professor of Anesthesiology

University of Pennsylvania

United States

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Emily Mackay is an Assistant Professor of Anesthesiology at the University of Pennsylvania. Her research primarily focuses on the application of artificial intelligence in anesthesiology, intraoperative echocardiography, and cardiac surgery practices. Recent articles co-authored by Dr. Mackay include studies on the automation of echocardiography reports and evaluating practice pattern variability in cardiac surgical settings.

Articles (11)

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2023

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2022

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Michael Burns

University of Michigan

UNITED STATES

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Richard Epstein

Professor

University of Miami Miller School of Medicine

UNITED STATES

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EMILY MACKAY

Research Interests

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Articles (11)

Practice Pattern Variability in the Use of Pulmonary Arterial Catheters in Cardiac Surgery

Automated structured data extraction from intraoperative echocardiography reports using large language models

Strengthening Discovery and Application of Artificial Intelligence in Anesthesiology: A Report from the Anesthesia Research Council

Strengthening Discovery and Application of Artificial Intelligence in Anesthesiology: A Report from the Anesthesia Research Council

Testing clinical selection criteria for intraoperative transoesophageal echocardiography in isolated coronary artery bypass graft surgery

The Society of Thoracic Surgeons Adult Cardiac Surgery Database: Intraoperative Echocardiography Reporting

Pragmatic Evaluation of a Deep-Learning Algorithm to Automate Ejection Fraction on Hand-Held, Point-of-Care Echocardiography in a Cardiac Surgical Operating Room

Predictors of Intraoperative Echocardiography: Analysis of The Society of Thoracic Surgeons Database

Impact of Hospital Practice and Staffing Differences on Transesophageal Echocardiography Use in Cardiac Valve or Coronary Artery Bypass Graft Surgery

Comprehensive Monitoring in Patients With Dual Lumen Right Atrium to Pulmonary Artery Right Ventricular Assist Device

Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations

Collaborators (7)

Bo Zhang

Jonathan Wanderer

Parisa Rashidi

Patrick Tighe

Hannah Lonsdale

Michael Burns

Richard Epstein

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