Parsimonious machine learning models to predict resource use in cardiac surgery across a statewide collaborativeCentral MessagePerspective

Objective: We sought to several develop parsimonious machine learning models to predict resource utilization and clinical outcomes following cardiac operations using only preoperative factors. Methods: All patients undergoing coronary artery bypass grafting and/or valve operations were identified in...

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Bibliographic Details

Published in:	JTCVS open Vol. 11; pp. 214 - 228
Main Authors:	Arjun Verma, Yas Sanaiha, MD, Joseph Hadaya, MD, Anthony Jason Maltagliati, MD, Zachary Tran, MD, Ramin Ramezani, PhD, Richard J. Shemin, MD, Peyman Benharash, MD, Peyman Benharash, MD, FACS, Richard J. Shemin, MD, FACS, Nancy Satou, Tom Nguyen, MD, Carolyn Clary, Michael Madani, MD, FACS, Jill Higgins, Dawna Steltzner, Bob Kiaii, MD, FRCSC, FACS, J. Nilas Young, MD, FACS, Kathleen Behan, Heather Houston, Cindi Matsumoto, Jack C. Sun, MD, MS, FRCSC, Lisha Flavin, Patria Fopiano, Maricel Cabrera, Rakan Khaki, MPH, Polly Washabaugh, BS
Format:	Journal Article
Language:	English
Published:	Elsevier 01-09-2022
Subjects:	cardiac surgery COVID-19 length of stay machine learning resource utilization
Online Access:	Get full text
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