Activities, Clubs, Etc:
Dartmouth Emergency Medical Services, Health Access for All, Memory Café, Undergraduate Resident Advisor, Study Group Leader
Culminating project or Thesis:
Department of Computer Science. It is titled "A Data-Driven Approach to Predict Carbohydrate Counting Errors in Diabetes Management"
In individuals with type 1 diabetes (T1D), carbohydrate counting, which refers to estimating the carbohydrate content in meals, is critical for determining mealtime insulin doses and maintaining healthy blood glucose levels. However, errors in carbohydrate counting (i.e., over-or under-estimation of carbohydrate intake) are very common and are often a source of poor glucose control. In this study, I use adverse glycemic events following meal intakes as a proxy for identifying carbo-hydrate counting errors, then use supervised machine learning models to predict these carbohydrate counting errors.