Abstract
Background and Aims
Process mining (PM) is a knowledge extraction technique that aims to let us understand how processes are performing. PM is useful for gaining insights into how patients are self-managing and identifying the actions they perform effectively, as well as the actions that should be avoided. We aim to discover and compare blood glucose regulation processes using the indirect problem approach and process mining.
Methods
We first defined the in-day process of blood glucose (BG) regulation according to lifestyle observable variables (carbohydrate intake, insulin administration, physical activity and blood glucose levels). We mined the processes in a dataset containing recordings from Continuous Glucose Monitoring systems and self-recorded data. The observed time span was restricted to one month of observations. Main endpoints are to discover networks of events that affect the Time In Range (TIR) and the effect of insulin/food intake and physical activity on BG.
Results
The application of process mining allowed discovering significant differences in the observable BG regulation processes and thus helping to estimate and quantify the effect of insulin, food intake and physical activity over time. The process oriented view allowed to cluster different behaviors based on the TIR and transitions from BG ranges after insulin, carbohydrate and physical activity, providing more insights than time-oriented views and first order descriptors.
Conclusions
This is the first step towards discovering blood glucose regulation processes using the indirect problem approach and process mining techniques. Our results will produce new knowledge towards the improvement of closed-loop systems and optimization of self-management strategies
