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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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| Publicado no: | Transl Androl Urol |
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| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
AME Publishing Company
2021
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7947454/ https://ncbi.nlm.nih.gov/pubmed/33718073 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.21037/tau-20-1208 |
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