Leading Institutions to Focus on Improving Type 1 Diabetes Care with Machine Learning

Machine learning is an entirely new approach to health analytics because it can generate robust insights from unstructured and imperfect data; such as the free text notes found throughout electronic health records. Validated by over a decade of research and clinical applications, Cyft 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. The three-year project is funded by a grant from the Helmsley Charitable Trust, a foundation that seeks to improve lives by supporting exceptional efforts in the U.S. and around the world. "Advancing care for type 1 diabetes has traditionally been difficult as we are working to better understand the impact of clinical and sociodemographic risk factors on outcomes, while also incorporating these insights into patient management strategies," said Mark Clements, MD, Ph.D. "Due to the development of machine learning technologies we can now make these data points immediately useful to individuals who are delivering care, not just those conducting research. This project aims to not only prove we can generate accurate type 1 diabetes learning models, but also use this information to proactively improve health outcomes and impact the wider type 1 diabetes community."