Machine learning and decision making in aortic arch repair.

Journal: The Journal of thoracic and cardiovascular surgery

PMID: 38016622

Abstract

BACKGROUND: Decision making during aortic arch surgery regarding cannulation strategy and nadir temperature are important in reducing risk, and there is a need to determine the best individualized strategy in a data-driven fashion. Using machine learning (ML), we modeled the risk of death or stroke in elective aortic arch surgery based on patient characteristics and intraoperative decisions.

Authors

Rashmi Nedadur

Division of Cardiac Surgery, Schulich Heart Centre; Department of Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.
Nitish Bhatt
Jennifer Chung

Peter Munk Cardiac Center, Toronto General Hospital, Toronto, Ontario, Canada.
Michael W A Chu
Maral Ouzounian

Peter Munk Cardiac Center, Toronto General Hospital, Toronto, Ontario, Canada. Electronic address: maral.ouzounian@uhn.ca.
Bo Wang

Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.

Keywords

Aged Aorta, Thoracic Clinical Decision-Making Decision Support Techniques Elective Surgical Procedures Female Humans Machine Learning Male Middle Aged Retrospective Studies Risk Assessment Risk Factors Stroke Treatment Outcome Vascular Surgical Procedures

External Resources

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Machine learning and decision making in aortic arch repair.

Abstract

Authors

Keywords

External Resources

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