Learning Temporal State of Diabetes Patients via Combining Behavioral and Demographic Data

Diabetes is a serious disease affecting a large number of people. Although there is no cure for diabetes, it can be managed. Especially, with advances in sensor technology, lots of data may lead to the improvement of diabetes management, if properly mined. However, there usually exists noise or errors in the observed behavioral data which poses challenges in extracting meaningful knowledge. To overcome this challenge, we learn the latent state which represents the patient's condition. Such states should be inferred from the behavioral data but unknown a priori. In this paper, we propose a novel framework to capture the trajectory of latent states for patients from behavioral data while exploiting their demographic differences and similarities to other patients. We conduct a hypothesis test to illustrate the importance of the demographic data in diabetes management, and validate that each behavioral feature follows an exponential or a Gaussian distribution. Integrating these aspects, we use a Demographic feature restricted hidden Markov model (DfrHMM) to estimate the trajectory of latent states by integrating the demographic and behavioral data. In DfrHMM, the latent state is mainly determined by the previous state and the demographic features in a nonlinear way. Markov Chain Monte Carlo techniques are used for model parameter estimation. Experiments on synthetic and real datasets show that DfrHMM is effective in diabetes management.