Taking a byte out of <scp>APRICOT</scp> to predict which children are at low risk for critical perioperative events-Reference-Cited by-同舟云学术

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Taking a byte out of APRICOT to predict which children are at low risk for critical perioperative events

Published:2023-06-13 Issue:9 Volume:33 Page:687-688
ISSN:1155-5645
Container-title:Pediatric Anesthesia
language:en
Short-container-title:Pediatric Anesthesia

Author:

Simpao Allan F.¹²^ORCID,Tsui Fu‐Chiang Rich¹²^ORCID,Matava Clyde T.³⁴^ORCID

Affiliation:

1. Department of Anesthesiology and Critical Care Medicine Children's Hospital of Philadelphia Philadelphia Pennsylvania USA

2. Department of Anesthesiology and Critical Care Perelman School of Medicine at the University of Pennsylvania Philadelphia Pennsylvania USA

3. Department of Anesthesia and Pain Medicine Hospital for Sick Children Toronto Ontario Canada

4. Department of Anesthesiology and Pain Medicine, Termerty Faculty of Medicine University of Toronto Toronto Ontario Canada

Publisher

Wiley

Subject

Anesthesiology and Pain Medicine,Pediatrics, Perinatology and Child Health

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/pan.14707

Reference6 articles.

1. Big data and visual analytics in anaesthesia and health care

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

3. A machine‐learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset

4. Incidence of severe critical events in paediatric anaesthesia (APRICOT): a prospective multicentre observational study in 261 hospitals in Europe

5. Airway management in paediatric anaesthesia in Europe—insights from APRICOT (Anaesthesia Practice In Children Observational Trial): a prospective multicentre observational study in 261 hospitals in Europe

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