Improving Quality in Cardiothoracic Surgery: Exploiting the Untapped Potential of Machine Learning
Ann Thorac Surg
.
2022 Dec;114(6):1995-2000.
doi: 10.1016/j.athoracsur.2022.06.058.
Epub 2022 Aug 4.
Authors
Agni Orfanoudaki
1
,
Joseph A Dearani
2
,
David M Shahian
3
,
Vinay Badhwar
4
,
Felix Fernandez
5
,
Robert Habib
6
,
Michael E Bowdish
7
,
Dimitris Bertsimas
8
Affiliations
1
Saïd Business School, Oxford University, Oxford, United Kingdom. Electronic address: agni.orfanoudaki@sbs.ox.ac.uk.
2
Department of Cardiovascular Surgery, Mayo Clinic, Rochester, Minnesota.
3
Division of Cardiac Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
4
Department of Cardiovascular and Thoracic Surgery, West Virginia University, Morgantown, West Virginia.
5
Division of Cardiothoracic Surgery, Emory University School of Medicine Atlanta, Georgia.
6
The Society of Thoracic Surgeons, Chicago, Illinois.
7
Department of Cardiac Surgery, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California.
8
Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts.
PMID:
35934068
DOI:
10.1016/j.athoracsur.2022.06.058
No abstract available
Publication types
Editorial
MeSH terms
Humans
Machine Learning
Specialties, Surgical*
Thoracic Surgery*