Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning

Frontiers in Pediatrics

James Fackler¹  Jules P. Bergmann¹  Anthony A. Sochet²  Joseph L. Greenstein³  Kirby D. Gong³  Raimond L. Winslow³  Joshua A. Krachman³  Jina Park³  Indranuj Gangan³  Christopher T. Le³  Ruijing Zhang³  Jessica A. Patricoski⁴ 

[1] Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States;Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States;Division of Pediatric Critical Care Medicine, Department of Pediatrics, Johns Hopkins All Children's Hospital, St Petersburg, FL, United States;Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States;Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States;Division of Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, MD, United States;

关键词: high flow nasal cannula;    flow rate escalation;    pediatric critical care;    non-response;    machine learning;    acute respiratory failure;   

DOI  :  10.3389/fped.2021.734753

来源: Frontiers

【 授权许可】

CC BY