AI mines EHR data to predict diabetic patients at risk for kidney damage, study finds

Artificial intelligence start-up Medial EarlySign in a new study has shown how the combination of AI and EHR data can facilitate early detection and treatment of kidney problems and can help slow down – or even prevent – progression to end-stage renal disease. Medial EarlySign's machine learning-based model analyzed dozens of factors residing in electronic health records, including laboratory test results, demographics, medications, diagnostic codes and others, to predict who might be at high risk for having renal dysfunction within one year. By isolating less than 5 percent of the 400,000 diabetic population selected among the company's database of 15 million patients, the algorithm was able to identify 45 percent of patients who would progress to significant kidney damage within a year, prior to becoming symptomatic, the start-up reported. This represents 25 percent more patients than would have been identified by commonly used clinical tools and judgment, the company contended. "Immense efforts are invested in developing treatment protocols to reduce the number of patients who will develop renal dysfunction due to diabetes," said Ran Goshen, MD, Medial EarlySign's chief medical officer.