Modulai Blog

Background

Machine learning (ML) in the healthcare sector is an interesting area with a lot of important applications. In this post, we give an overview of a study conducted in collaboration with Daniel Espes and Per-Ola Carlsson at Uppsala University aiming to improve the treatment of type 1 diabetes. The primary aim was to predict the root cause of hypoglycemic events (i.e. periods of low blood glucose) using time series data consisting of Continuous Glucose Measurements (CGM).

Furthermore, interpreting how the deep learning model makes predictions based on the CGM time series alone could improve the scientific understanding of CGM classification and help clinicians understand the patients’ glucose measurements in a more refined way. Model interpretation is an active research area within ML and may be applied also to other clinical applications from a broader perspective. 

The team on Modulai’s side consisted of Gustav Eklund and Amund Vedal as machine learning engineers and Puya Sharif and Josef Lindman Hörnlund as project advisors.

This post might be updated in the future with results and performance metrics contingent on the submission and acceptance of the research paper.