Gaussian process regression with categorical inputs for predicting the blood glucose level

Abstract

In diabetes treatment, the blood glucose level is key quantity for evaluating patient’s condition. Typically, measurements of the blood glucose level are recorded by patients and they are annotated by symbolic quantities, such as, date, timestamp, measurement code (insulin dose, food intake, exercises). In clinical practice, predicting the blood glucose level for different conditions is an important task and plays crucial role in personalized treatment. This paper describes a predictive model for the blood glucose level based on Gaussian processes. The covariance function is proposed to deal with categorical inputs. The usefulness of the presented model is demonstrated on real-life datasets concerning 10 patients. The results obtained in the experiment reveal that the proposed model has small predictive error measured by the Mean Absolute Error criterion even for small training samples.