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Title: Continuous Intraoperative Data Analysis Using Machine Learning Reveals Multiple Parameters to Predict Post-CABG Renal Failure
The purpose of this study is to utilize machine learning techniques to identify intraoperative parameters that contribute significantly to the development of postoperative renal failure following CABG and predict postoperative renal failure based on these parameters. Continuous intraoperative data were gathered retrospectively from the anaesthesia record and included hemodynamic information such as heart rate, arterial blood pressure, central venous pressure, pulmonary artery pressure, as well as additional information such as ventilator settings, temperature, and medication or fluid administration. Multiple machine learning algorithms were tested with this dataset using 10 fold cross validation with stratified folds and their classification performance was measured using area under the receiver operating characteristic curves (ROC AUC). Continuous intraoperative data gathered from patients undergoing CABG revealed potential targets for early, intraoperative intervention to prevent the development of postoperative renal failure.  more » « less
Award ID(s):
1950811
PAR ID:
10339368
Author(s) / Creator(s):
Date Published:
Journal Name:
The Society of Thoracic Surgeons Annual Meeting
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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