This increased demand for CGM devices means an opportunity for data and computer scientists, who can contribute to the development of decision-making support systems based on the data collected from the devices. Our aim is to applied Machine Learning techniques to this data and find patterns that leads to advisory systems.
Using both the entered data and the blood glucose values collected by the device automatically, the application presented here uses decision trees to detect the patterns and entails a starting point in the creation of ensemble models with more predictive power, also based on decision trees. Furthermore, the methodology makes a segmentation of the data set in blocks, determined by the different meals done throughout the day, adding more information to the set of variables used to train the models.
The application developed in this project generates a report of the patient’s glucose patterns and provides a web application that allows the user to upload the data obtained from his device and download the report on his computer or smartphone.
As a result, the application can discover repetitive patterns in the daily life of the patient, which can help him to take early preventive measures for risk situations in a period close to the next meal.
Thanks: RTI2018-095180-B-I00 and Fundación Eugenio Rodriguez Pascual