Medial EarlySign touted data today from a study of its algorithm designed to identify diabetic patients who have the highest risk of developing renal dysfunction.
The company’s machine-learning model looks at data from electronic health records to make predictions about patients’ health. In this study, the company reported that the algorithm identified 45% of patients who experienced significant kidney damage within one year – 25% more patients than commonly-used clinical tools would have detected, according to Medial EarlySign.
“Immense efforts are invested in developing treatment protocols to reduce the number of patients who will develop renal dysfunction due to diabetes,” chief medical officer Dr. Ran Goshen said in prepared remarks. “Medial EarlySign’s algorithm can aid decision-makers, drug developers, insurers and providers to better allocate their capped resources and secure preferential clinical outcome as well. This can help reduce the likelihood for diabetes-related end-stage renal disease (ESRD).”
“The significant size and rapid growth of digital health databases now allow the application of advanced mathematical tools that can identify patterns in diverse patient populations in order to identify high-risk patients,” Dr. Itamar Raz, director emeritus of Hadassah University Hospital’s diabetes unit, added.
“Rather than relying only on small patient samples based on known risk factors, machine learning tools can reveal the slightest correlations among these parameters and discover additional risk indicators that can lead to improved prediabetic patient risk stratification.”
Kidney problems commonly plague people with diabetes, according to Medial EarlySign – in the U.S. between 2011 and 2012, more than 36% of adults with diabetes had diabetic nephropathy or some form of renal dysfunction.
