Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement.

Journal: The Journal of surgical research

Published Date: Aug 1, 2018

Abstract

BACKGROUND: Early identification of critically ill patients who will require prolonged mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use machine learning to identify patients at risk for PMV and tracheostomy placement.

Authors

Joshua Parreco

Department of Surgery, University of Miami Leonard M. Miller School of Medicine, Miami, Florida.
Antonio Hidalgo

DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida.
Jonathan J Parks

DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida.
Robert Kozol

DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida.
Rishi Rattan

Division of Trauma Surgery and Surgical Critical Care, Department of Surgery, University of Miami Miller School of Medicine, USA. Electronic address: rrattan@miami.edu.

Keywords

Critical Care Critical Illness Databases, Factual Decision Support Techniques Feasibility Studies Humans Intensive Care Units Length of Stay Patient Selection Respiration, Artificial Risk Assessment Severity of Illness Index Supervised Machine Learning Time Factors Tracheostomy Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (29907209)

Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement.

Abstract

Authors

Keywords

External Resources

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