Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15(th) ADQI Consensus Conference

The data contained within the electronic health record (EHR) is “big” from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI...

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Detalles Bibliográficos
Autores principales: Sutherland, Scott M., Chawla, Lakhmir S., Kane-Gill, Sandra L., Hsu, Raymond K., Kramer, Andrew A., Goldstein, Stuart L., Kellum, John A., Ronco, Claudio, Bagshaw, Sean M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4768420/
https://www.ncbi.nlm.nih.gov/pubmed/26925247
http://dx.doi.org/10.1186/s40697-016-0099-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4768420/
https://www.ncbi.nlm.nih.gov/pubmed/26925247
http://dx.doi.org/10.1186/s40697-016-0099-4

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