Kidney problems are one of the most common diabetes-related complications; approximately 20% – 40% of all diabetics worldwide will be affected by diabetic nephropathy, or renal dysfunction. Between 2011 – 2012, 36.5% of adults with diabetes in the U.S. had nephropathy. This includes chronic kidney disease (CKD) as well as end stage renal disease (ESRD). Diabetes is the leading cause of ESRD in the United States, Europe, and Japan. One of the biggest challenges in tackling this epidemic is to find and treat diabetics before they progress to nephropathy to potentially prevent the disease altogether.
Medial EarlySign’s preventative AI predictors strive to do just that. We are developing a machine learning-based AI algorithm to identify diabetic sub-populations at high risk for developing diabetic nephropathy within a specific time period. The model examines EHR and lab data, utilizing advanced algorithms and predictive analytics to find hidden anomalies that suggest signs of high risk for CKD. The model supports healthcare organizations’ efforts to allocate their resources more effectively, timely intervene with diabetics at high risk for nephropathy to potentially delay or halt its progression, and work towards improved patient outcomes.
In a retrospective clinical data study, Medial EarlySign’s diabetic nephropathy algorithm analyzed a variety of factors for CKD using data from a cohort of more than 500,000 diabetic patients. The study found that our algorithm identified more diabetics likely to develop kidney complications within one (1) year than would have been found via any known common medical practice.
Contact us for more details about Medial EarlySign’s diabetic nephropathy algorithm research.