Predicting Blood Glucose Dynamics with Multi-time-series Deep Learning

Abstract

Predicting blood glucose dynamics is vital for people to take preventive measures in time against health risks. Previous efforts adopt handcrafted features and design prediction models for each person, which result in low accuracy due to ineffective feature representation and the limited training data. This work proposes MT-LSTM, a multi-time-series deep LSTM model for accurate and efficient blood glucose concentration prediction. MT-LSTM automatically learns feature representations and temporal dependencies of blood glucose dynamics by jointly sharing data among multiple users and utilizes an individual learning layer for personalized prediction. Evaluations on 112 users demonstrate that MT-LSTM significant outperform conventional predictive regression models.