Parsimonious machine learning models to predict resource use in cardiac surgery across a statewide collaborative-Reference-Cited by-同舟云学术

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

Published:2022-09 Issue: Volume:11 Page:214-228
ISSN:2666-2736
Container-title:JTCVS Open
language:en
Short-container-title:JTCVS Open

Author:

Verma Arjun,Sanaiha Yas,Hadaya Joseph,Maltagliati Anthony Jason,Tran Zachary,Ramezani Ramin,Shemin Richard J.,Benharash Peyman,Benharash Peyman,Shemin Richard J.,Satou Nancy,Nguyen Tom,Clary Carolyn,Madani Michael,Higgins Jill,Steltzner Dawna,Kiaii Bob,Young J. Nilas,Behan Kathleen,Houston Heather,Matsumoto Cindi,Sun Jack C.,Flavin Lisha,Fopiano Patria,Cabrera Maricel,Khaki Rakan,Washabaugh Polly

Publisher

Elsevier BV

Subject

Cardiology and Cardiovascular Medicine,Pulmonary and Respiratory Medicine,Surgery

Reference30 articles.

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2. Cardiac surgery outcomes in an epicenter of the COVID-19 pandemic;Farrington;Semin Thorac Cardiovasc Surg,2022

3. Cardiac surgery in North America and coronavirus disease 2019 (COVID-19): regional variability in burden and impact;Ad;J Thorac Cardiovasc Surg,2021

4. The rapid transformation of cardiac surgery practice in the coronavirus disease 2019 (COVID-19) pandemic: insights and clinical strategies from a centre at the epicentre;George;Eur J Cardiothorac Surg,2020

5. The cost of quarantine: projecting the financial impact of canceled elective surgery on the nation’s hospitals;Bose;Ann Surg,2021

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