Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease

AIM: Predicting progression in diabetic kidney disease (DKD) is critical to improving outcomes. We sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining electronic health records (EHR) and biomarkers. METHODS: This is an observational cohort study of patients...

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Detalles Bibliográficos
Autores principales: Chan, Lili, Nadkarni, Girish N., Fleming, Fergus, McCullough, James R., Connolly, Patricia, Mosoyan, Gohar, El Salem, Fadi, Kattan, Michael W., Vassalotti, Joseph A., Murphy, Barbara, Donovan, Michael J., Coca, Steven G., Damrauer, Scott M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187208/
https://www.ncbi.nlm.nih.gov/pubmed/33797560
http://dx.doi.org/10.1007/s00125-021-05444-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187208/
https://www.ncbi.nlm.nih.gov/pubmed/33797560
http://dx.doi.org/10.1007/s00125-021-05444-0

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