Prediction and Analysis of Length of Stay Based on Nonlinear Weighted XGBoost Algorithm in Hospital

An improved nonlinear weighted extreme gradient boosting (XGBoost) technique is developed to forecast length of stay for patients with imbalance data. The algorithm first chooses an effective technique for fitting the duration of stay and determining the distribution law and then optimizes the negat...

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Detalles Bibliográficos
Autor principal: Chen, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654524/
https://www.ncbi.nlm.nih.gov/pubmed/34900191
http://dx.doi.org/10.1155/2021/4714898

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654524/
https://www.ncbi.nlm.nih.gov/pubmed/34900191
http://dx.doi.org/10.1155/2021/4714898

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