Machine learning-assisted decision-support models to better predict patients with calculous pyonephrosis

BACKGROUND: To develop a machine learning (ML)-assisted model capable of accurately identifying patients with calculous pyonephrosis before making treatment decisions by integrating multiple clinical characteristics. METHODS: We retrospectively collected data from patients with obstructed hydronephr...

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Detalles Bibliográficos
Autores principales: Liu, Hailang, Wang, Xinguang, Tang, Kun, Peng, Ejun, Xia, Ding, Chen, Zhiqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947454/
https://www.ncbi.nlm.nih.gov/pubmed/33718073
http://dx.doi.org/10.21037/tau-20-1208

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947454/
https://www.ncbi.nlm.nih.gov/pubmed/33718073
http://dx.doi.org/10.21037/tau-20-1208

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