Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications.

Journal: JAMA network open

Published Date: Mar 1, 2021

IMPORTANCE: Postoperative complications can significantly impact perioperative care management and planning.

Bing Xue
Dingwen Li

Department of Computer Science and Engineering, McKelvey School of Engineering, Washington University in St Louis, St Louis, Missouri.
Chenyang Lu

Department of Computer Science and Engineering, Washington University, St. Louis, MO.
Christopher R King

Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA.
Troy Wildes

Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA.
Michael S Avidan

Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA.
Thomas Kannampallil

Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, USA.
Joanna Abraham

Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA.

Decision Support Systems, Clinical Female Follow-Up Studies Humans Incidence Intraoperative Period Machine Learning Male Middle Aged Postoperative Complications Preoperative Period Retrospective Studies Risk Assessment Risk Factors ROC Curve United States

View on PubMed Access via DOI PubMed (33783520)