Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy

BACKGROUND: There are many Machine Learning (ML) models which predict acute kidney injury (AKI) for hospitalised patients. While a primary goal of these models is to support clinical decision-making, the adoption of inconsistent methods of estimating baseline serum creatinine (sCr) may result in a p...

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Detalles Bibliográficos
Autores principales: Kamel Rahimi, Amir, Ghadimi, Moji, van der Vegt, Anton H., Canfell, Oliver J., Pole, Jason D., Sullivan, Clair, Shrapnel, Sally
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10563357/
https://www.ncbi.nlm.nih.gov/pubmed/37814311
http://dx.doi.org/10.1186/s12911-023-02306-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10563357/
https://www.ncbi.nlm.nih.gov/pubmed/37814311
http://dx.doi.org/10.1186/s12911-023-02306-0

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