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

Abstract

Authors

• Lili Chan
  Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
• Girish N Nadkarni
  Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
• Fergus Fleming
  Renalytix AI Plc, Cardiff, UK.
• James R McCullough
  Renalytix AI Plc, Cardiff, UK.
• Patricia Connolly
  Renalytix AI Plc, Cardiff, UK.
• Gohar Mosoyan
  Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
• Fadi El Salem
  Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
• Michael W Kattan
• Joseph A Vassalotti
  Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
• Barbara Murphy
  Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
• Michael J Donovan
  Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
• Steven G Coca
  Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
• Scott M Damrauer
  Department of Surgery, Perelman School of Medicine at University of Pennsylvania, Philadelphia, PA, USA. damrauer@upenn.edu.

Keywords

External Resources