Machine learning: implications and applications for ambulatory anesthesia.

Journal: Current opinion in anaesthesiology

PMID: 38979675

Abstract

PURPOSE OF REVIEW: This review explores the timely and relevant applications of machine learning in ambulatory anesthesia, focusing on its potential to optimize operational efficiency, personalize risk assessment, and enhance patient care.

Authors

Karisa Anand

University of Texas Southwestern.
Suk Hong

Department of Anesthesiology and Pain Management.
Kapil Anand

University of Texas Southwestern, Department of Anesthesiology and Pain Management, Dallas.
Joseph Hendrix

Worldwide Clinical Trials, San Antonio, Texas, USA.

Keywords

Ambulatory Surgical Procedures Anesthesia Anesthesiology Humans Length of Stay Machine Learning Risk Assessment

External Resources

View on PubMed Access via DOI PubMed (38979675)

Machine learning: implications and applications for ambulatory anesthesia.

Abstract

Authors

Keywords

External Resources

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