A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia.

Shi, Gaoxiang; Liu, Geliang; Gao, Qichao; Zhang, Shengxiao; Wang, Qi; Wu, Li; He, Peifeng; Yu, Qi

Shi, Gaoxiang; Liu, Geliang; Gao, Qichao; Zhang, Shengxiao; Wang, Qi; Wu, Li; He, Peifeng; Yu, Qi.

Afiliação

Shi G; School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.
Liu G; Institute of Medical Data Science, Shanxi Medical University, Taiyuan, China.
Gao Q; Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China.
Zhang S; Department of Anesthesiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China.
Wang Q; Institute of Medical Data Science, Shanxi Medical University, Taiyuan, China.
Wu L; Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China.
He P; School of Management, Shanxi Medical University, Taiyuan, China.
Yu Q; School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.

BMC Anesthesiol ; 23(1): 361, 2023 11 06.

Article em En | MEDLINE | ID: mdl-37932714

Assuntos

Procedimentos Ortopédicos; Algoritmo Florestas Aleatórias; Humanos; Dor Pós-Operatória; Fatores de Risco

Palavras-chave

Analgesia; Logistic regression model; Machine learning; Pain prediction; Random forest model

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Procedimentos Ortopédicos / Algoritmo Florestas Aleatórias Limite: Humans Idioma: En Revista: BMC Anesthesiol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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