Patterns Detection in Glucose Time Series by Domain Transformations and Deep Learning
This addresses a critical health management issue for people with diabetes, but it appears incremental as it builds on existing deep learning methods with domain-specific adaptations.
The paper tackled the problem of predicting future blood glucose levels to anticipate hypoglycemic events in diabetes patients, using transformation functions on glucose time series and convolutional neural networks, achieving promising results on real data from 4 patients.
People with diabetes have to manage their blood glucose level to keep it within an appropriate range. Predicting whether future glucose values will be outside the healthy threshold is of vital importance in order to take corrective actions to avoid potential health damage. In this paper we describe our research with the aim of predicting the future behavior of blood glucose levels, so that hypoglycemic events may be anticipated. The approach of this work is the application of transformation functions on glucose time series, and their use in convolutional neural networks. We have tested our proposed method using real data from 4 different diabetes patients with promising results.