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 wi...

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Veröffentlicht in:	Diabetologia 2021-07, Vol.64 (7), p.1504-1515
Hauptverfasser:	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.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Aged Aged, 80 and over Biomarkers Biomarkers - analysis Cohort Studies Creatinine Diabetes Diabetes mellitus Diabetic Nephropathies - diagnosis Diabetic Nephropathies - epidemiology Diabetic Nephropathies - pathology Diabetic nephropathy Disease Progression Electronic health records Electronic Health Records - statistics & numerical data Electronic medical records Epidermal growth factor receptors Female Glomerular Filtration Rate Human Physiology Humans Internal Medicine Kidney diseases Kidney Function Tests - statistics & numerical data Learning algorithms Machine Learning Male Medicine Medicine & Public Health Metabolic Diseases Middle Aged Predictive Value of Tests Prognosis Reclassification Renal failure Risk Factors Risk groups United States - epidemiology Young Adult
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