Machine Learning Project Targets Type 1 Diabetes Intervention

June 19, 2017
Children's Mercy Kansas City and Joslin Diabetes Center will soon deploy machine learning-enabled solutions to proactively manage health outcomes in patients with type 1 diabetes.

Children's Mercy Kansas City and Joslin Diabetes Center will soon deploy machine learning-enabled solutions to proactively manage health outcomes in patients with type 1 diabetes (T1D) at two independent diabetes clinics.

Utilizing technology pioneered by a Cambridge, Mass.-based company called Cyft Inc., the project will work to optimize aspects of diabetes management by supplying novel information to clinical staff at the point of treatment. The three-year project is funded by a grant from the Helmsley Charitable Trust.

Four industry leaders will guide the project as they seek to create, evaluate, and deploy predictive models at the two selected clinics:

• Mark Clements, M.D., Ph.D., Medical Director for the Pediatric Clinical Research Unit at Children's Mercy Kansas City

• Sanjeev Mehta, M.D., M.P.H., Joslin Diabetes Center's Chief Medical Information Officer and Director of Quality

• Leonard D'Avolio, Ph.D., CEO and founder of Cyft and Assistant Professor at Harvard Medical School and Brigham and Women's Hospital; and

• Susana Patton, Ph.D., Associate Professor of Pediatrics at the University of Kansas Medical Center

Cyft’s technology will employ machine learning and natural language processing as well as device signal processing to analyze multiple data sources and create predictive models for use by health professionals. These models will detect and alert caregivers to opportunities to intervene in the care of patients at risk for deterioration in their health outcomes

T1D is the second most prevalent chronic disease of childhood after asthma. "For individuals living with T1D, we have learned much about risk factors for suboptimal health outcomes, but there remain significant opportunities to proactively identify and engage our patients who are at risk for future deterioration,” said Dr. Mehta in a prepared statement. “Predictive analytics holds promise in this area as well as in the identification of novel clusters of patient factors that could identify high-risk patients. This learning health system will further our goal of leveraging the power of our data to identify and proactively support patients who are at risk for clinical deterioration to positively impact their health and general well-being."

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