Diabetes Meets Machine Learning, Part 1

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.Of all the disorders that have responded well to artificial intelligence and machine learning, diabetes mellitus probably tops the list. The evidence supporting a role for Machine Learning (ML)-enhanced algorithms in managing the disease is persuasive and applies to several components of patient care, including screening, diagnosis, treatment, and prognosis.Let ' s start with screening: As most clinicians know, there ' s a difference between screening for disease and diagnosing it. The former casts a much wider net, which means it will include more false positives. Once this larger cohort has been identified, more precise diagnostic testing can be performed to pinpoint patients who have the disorder.  As we explain in our next book,The Digital Reconstruction of Healthcare,screening tools for diabetes have been in existence for many years, including tools to help clinicians and patients identify the presence of prediabetes. Still, new technology has taken the screening process to a new level.The American Diabetes Association (ADA) defines prediabetes as fasting plasma glucose between 100 and 126 mg/dl, a 2-hour oral glucose tolerance test reading between 140 and 200 mg/dl, or hemoglobin A1c of 5.7% to 6.5%.1 The Association has developed a risk assessment tool to help clinicians and patients; it asks for a p...
Source: Life as a Healthcare CIO - Category: Information Technology Source Type: blogs