Machine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing.

Tully, Jeffrey L; Zhong, William; Simpson, Sierra; Curran, Brian P; Macias, Alvaro A; Waterman, Ruth S; Gabriel, Rodney A

Tully, Jeffrey L; Zhong, William; Simpson, Sierra; Curran, Brian P; Macias, Alvaro A; Waterman, Ruth S; Gabriel, Rodney A.

Afiliação

Tully JL; Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, USA. jtully@health.ucsd.edu.
Simpson S; Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, USA.
Curran BP; Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, USA.
Macias AA; Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, USA.
Waterman RS; Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, USA.
Gabriel RA; Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, USA.

J Med Syst ; 47(1): 71, 2023 Jul 10.

Article em En | MEDLINE | ID: mdl-37428267

Assuntos

Procedimentos Cirúrgicos Ambulatórios; Período de Recuperação da Anestesia; Humanos; Tempo de Internação; Salas Cirúrgicas; Aprendizado de Máquina

Palavras-chave

Artificial intelligence; Machine learning; Outpatient surgery; Perioperative informatics; Perioperative resource management

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Período de Recuperação da Anestesia / Procedimentos Cirúrgicos Ambulatórios Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Med Syst Ano de publicação: 2023 Tipo de documento: Article

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