Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications-Reference-Cited by-同舟云学术

Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications

Published:2024-06 Issue:6 Volume:132 Page:1315-1326
ISSN:0007-0912
Container-title:British Journal of Anaesthesia
language:en
Short-container-title:British Journal of Anaesthesia

Author:

Li Peiyi,Gao Shuanliang,Wang Yaqiang,Zhou RuiHao,Chen Guo,Li Weimin,Hao Xuechao,Zhu Tao^ORCID

Publisher

Elsevier BV

Reference44 articles.

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3. Association of postoperative complications with hospital costs and length of stay in a tertiary care center;Khan;J Gen Intern Med,2006

4. Epidemiology, practice of ventilation and outcome for patients at increased risk of postoperative pulmonary complications: LAS VEGAS – an observational study in 29 countries;Eur J Anaesthesiol,2017

5. Postoperative pulmonary complications;Miskovic;Br J Anaesth,2017

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1. Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications. Comment on Br J Anaesth 2024; 132: 1315–26;British Journal of Anaesthesia;2024-09

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