In their research around constant glucose monitoring and the automated maintenance of insulin for patients, the CDT is exploring data drawn from external data sources such as DexCom and FitBit. SOMRC has assisted the CDT by designing a secure computing footprint in Amazon Web Services to pull in these data, parse and process them, in order to perform deeper analytics through machine learning. In January 2018, CDT sponsored a ski camp at Wintergreen Resort for a group of youth diagnosed with Type I diabetes with the goal of importing glucose, insulin, and exercise metrics at the end of each day through remote web APIs.
Before patients are admitted to the emergency room, they are assigned a triage level based on the severity of their health problems. This is accomplished using the Emergency Severity Index (ESI), an emergency department triage algorithm that classifies patient cases into five different levels of urgency. Researchers are interested in using machine learning to develop a model to predict patient triage level. This model would not only analyze the typical vital signs that are used in the ESI, but also demographic data and patients’ history of health.