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Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods
BACKGROUND: Acute kidney injury (AKI) is diagnosed based on postoperative serum creatinine change, but AKI models have not consistently performed well, in part due to the omission of clinically important but practically unmeasurable variables that affect creatinine. We hypothesized that a latent var...
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| Опубликовано в: : | BMC Nephrol |
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| Главные авторы: | , , , , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
BioMed Central
2017
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5299779/ https://ncbi.nlm.nih.gov/pubmed/28178929 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12882-017-0465-1 |
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