Diabetes Meets Machine Learning, Part 2

Continuous glucose monitoring takes center stageJohn Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article.In February, we discussed the benefits ofusing machine learning (ML) to improve screening for diabetes and for managing the disorder. In some situations, ML can more accurately detect the presence of prediabetes, for instance. Similarly, there ’s research to show that the right algorithms can improve the treatment of Type 1 diabetes.  But there ’s also mounting evidence to suggest ML can benefit Type 2 patients.One of the problems clinicians and patients have in managing Type 2 diabetes is the fact that medical care is episodic.  Patients may see their physician or nurse practitioner once every few months, which means most of the time, they are on their own. And while it is true that glucose meters let patients monitor their blood glucose (BG) levels on a daily basis, those readings have their limitations. Many patients hesitate to take enough readings because finger pricks hurt. It can also be difficult to determine the need for changes in one ’s diet, medication, and physical activity based on a few daily BG readings. And while glycated hemoglobin A1c provides a picture of one ’s long-term metabolic control, it doesn’t provide immediate feedback on how to respond to day-to-day changes in food intake, stress levels, exercise and the like.&...
Source: Life as a Healthcare CIO - Category: Information Technology Source Type: blogs