Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms.

Zhou, Cheng-Mao; Wang, Ying; Xue, Qiong; Yang, Jian-Jun; Zhu, Yu

Zhou, Cheng-Mao; Wang, Ying; Xue, Qiong; Yang, Jian-Jun; Zhu, Yu.

Afiliação

Zhou CM; Department of Anaesthesiology, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China. zhouchengmao187@foxmail.com.
Wang Y; Anesthesia and Big Data Research Group, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China. zhouchengmao187@foxmail.com.
Xue Q; Department of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Yang JJ; Department of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Zhu Y; Department of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China. yjyangjj@126.com.

BMC Med Res Methodol ; 23(1): 133, 2023 05 31.

Article em En | MEDLINE | ID: mdl-37259031

Assuntos

Inteligência Artificial; Aprendizado Profundo; Humanos; Náusea e Vômito Pós-Operatórios; Haloperidol; Algoritmos; Aprendizado de Máquina

Palavras-chave

AUC; Deep learning; Machine learning; PONV; SVC

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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