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Comparison of Prediction Models for Acute Kidney Injury Among Patients with Hepatobiliary Malignancies Based on XGBoost and LASSO-Logistic Algorithms
BACKGROUND: Based on the admission data, we applied the XGBoost algorithm to create a prediction model to estimate the AKI risk in patients with hepatobiliary malignancies and then compare its prediction capacity with the logistic model. METHODS: We reviewed clinical data of 7968 and 589 liver/gallb...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Dove
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057825/ https://www.ncbi.nlm.nih.gov/pubmed/33889012 http://dx.doi.org/10.2147/IJGM.S302795 |