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Predicting outcomes of chronic kidney disease from EMR data based on Random Forest Regression
Chronic kidney disease (CKD) is prevalent across the world, and kidney function is well defined by an estimated glomerular filtration rate (eGFR). The progression of kidney disease can be predicted if the future eGFR can be accurately estimated using predictive analytics. In this study, we developed...
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| Vydáno v: | Math Biosci |
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| Hlavní autoři: | , , |
| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
2019
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| Témata: | |
| On-line přístup: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6435377/ https://ncbi.nlm.nih.gov/pubmed/30768948 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.mbs.2019.02.001 |
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