Project 3: Smart Insulin Parameter Optimization (SIPO)


Being a parent of a child with type 1 diabetes (T1D) is a kind of a hassle, even with being fortunate to acquire smart systems like continuous glucose monitoring system (CGM) and insulin pumps. Those are small computerized devices to mimic the way the human pancreas works by delivering small doses of short acting insulin continuously. However, these devices still need a lot of careful tuning and parameterization to deliver the correct amount of insulin at the right time. The parameters and factors’ values are basically calculated and set by diabetes doctors along with the discussion with the patient (parent in our case).

As still there is a substantial portion of the setting procedure done empirically, I take this as a strong motivation to leverage machine learning to at least give some insights. My motivation is also supported by the fact that those devices register considerable amount of data to learn from.

I share my reflections in a series of posts.


Part 1: How I use AWS SageMaker DeepAR to Manage my Child Diabetes ! : Introducing the Problem

Server IP: 16.162.17.243