Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care

Hannah Lonsdale; Ali Jalali; Luis Ahumada; Clyde Matava

doi:10.1016/j.jpeds.2020.02.039

Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care

J Pediatr. 2020 Jun:221S:S3-S10. doi: 10.1016/j.jpeds.2020.02.039.

Authors

Hannah Lonsdale¹, Ali Jalali², Luis Ahumada², Clyde Matava³

Affiliations

¹ Department of Anesthesia, Perioperative and Pain Medicine, Johns Hopkins All Children's Hospital, St. Petersburg, FL; Department of Anaesthesia, Sheffield Children's Hospital, Sheffield, United Kingdom.
² Health Informatics Core, Johns Hopkins All Children's Hospital, St. Petersburg, FL.
³ Department of Anesthesia and Pain Medicine, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Anesthesia, University of Toronto, Toronto, Ontario, Canada. Electronic address: clyde.matava@sickkids.ca.

PMID: 32482232
DOI: 10.1016/j.jpeds.2020.02.039

No abstract available

Keywords: airway; anesthesia; artificial intelligence; critical care; deep learning; diagnosis; intubation; machine learning; neural network; pediatric; pediatric anesthesia; perioperative; prognosis; transfusion medicine.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Airway Management
Algorithms
Anesthesia
Artificial Intelligence*
Biomedical Research
Blood Transfusion
Critical Care*
Heart Arrest / diagnosis
Hemorrhage / prevention & control
Hospital Mortality
Humans
Intensive Care Units, Pediatric
Intraoperative Complications / prevention & control
Machine Learning*
Monitoring, Intraoperative
Patient Admission
Perioperative Care*
Postoperative Complications / prevention & control
Risk Assessment
Ultrasonography