Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis

BACKGROUND AND AIMS: This study aimed to develop an interpretable random forest model for predicting severe acute pancreatitis (SAP). METHODS: Clinical and laboratory data of 648 patients with acute pancreatitis were retrospectively reviewed and randomly assigned to the training set and test set in...

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Detalles Bibliográficos
Autores principales: Hong, Wandong, Lu, Yajing, Zhou, Xiaoying, Jin, Shengchun, Pan, Jingyi, Lin, Qingyi, Yang, Shaopeng, Basharat, Zarrin, Zippi, Maddalena, Goyal, Hemant
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Cellular and Infection Microbiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9226542/
https://www.ncbi.nlm.nih.gov/pubmed/35755843
http://dx.doi.org/10.3389/fcimb.2022.893294

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9226542/
https://www.ncbi.nlm.nih.gov/pubmed/35755843
http://dx.doi.org/10.3389/fcimb.2022.893294

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