  Blood glucose value prediction is an important task in diabetes management.
While it is reported that glucose concentration is sensitive to social context
such as mood, physical activity, stress, diet, alongside the influence of
diabetes pathologies, we need more research on data and methodologies to
incorporate and evaluate signals about such temporal context into prediction
models. Person-generated data sources, such as actively contributed surveys as
well as passively mined data from social media offer opportunity to capture
such context, however the self-reported nature and sparsity of such data mean
that such data are noisier and less specific than physiological measures such
as blood glucose values themselves. Therefore, here we propose a Gaussian
Process model to both address these data challenges and combine blood glucose
and latent feature representations of contextual data for a novel multi-signal
blood glucose prediction task. We find this approach outperforms common methods
for multi-variate data, as well as using the blood glucose values in isolation.
Given a robust evaluation across two blood glucose datasets with different
forms of contextual information, we conclude that multi-signal Gaussian
Processes can improve blood glucose prediction by using contextual information
and may provide a significant shift in blood glucose prediction research and
practice.
