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Improving Quality in Cardiothoracic Surgery: Exploiting the Untapped Potential of Machine Learning.
Orfanoudaki, Agni; Dearani, Joseph A; Shahian, David M; Badhwar, Vinay; Fernandez, Felix; Habib, Robert; Bowdish, Michael E; Bertsimas, Dimitris.
Affiliation
  • Orfanoudaki A; Saïd Business School, Oxford University, Oxford, United Kingdom. Electronic address: agni.orfanoudaki@sbs.ox.ac.uk.
  • Dearani JA; Department of Cardiovascular Surgery, Mayo Clinic, Rochester, Minnesota.
  • Shahian DM; Division of Cardiac Surgery, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Badhwar V; Department of Cardiovascular and Thoracic Surgery, West Virginia University, Morgantown, West Virginia.
  • Fernandez F; Division of Cardiothoracic Surgery, Emory University School of Medicine Atlanta, Georgia.
  • Habib R; The Society of Thoracic Surgeons, Chicago, Illinois.
  • Bowdish ME; Department of Cardiac Surgery, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California.
  • Bertsimas D; Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Ann Thorac Surg ; 114(6): 1995-2000, 2022 12.
Article in En | MEDLINE | ID: mdl-35934068
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Specialties, Surgical / Thoracic Surgery Limits: Humans Language: En Journal: Ann Thorac Surg Year: 2022 Document type: Article Country of publication: Países Bajos
Fulltext
Add to My VHL
Print
XML
PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Specialties, Surgical / Thoracic Surgery Limits: Humans Language: En Journal: Ann Thorac Surg Year: 2022 Document type: Article Country of publication: Países Bajos