  Successful health risk prediction demands accuracy and reliability of the
model. Existing predictive models mainly depend on mining electronic health
records (EHR) with advanced deep learning techniques to improve model accuracy.
However, they all ignore the importance of publicly available online health
data, especially socioeconomic status, environmental factors, and detailed
demographic information for each location, which are all strong predictive
signals and can definitely augment precision medicine. To achieve model
reliability, the model needs to provide accurate prediction and uncertainty
score of the prediction. However, existing uncertainty estimation approaches
often failed in handling high-dimensional data, which are present in
multi-sourced data. To fill the gap, we propose UNcertaInTy-based hEalth risk
prediction (UNITE) model. Building upon an adaptive multimodal deep kernel and
a stochastic variational inference module, UNITE provides accurate disease risk
prediction and uncertainty estimation leveraging multi-sourced health data
including EHR data, patient demographics, and public health data collected from
the web. We evaluate UNITE on real-world disease risk prediction tasks:
nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD). UNITE
achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for
NASH detection, and outperforms various state-of-the-art baselines by up to
$19\%$ over the best baseline. We also show UNITE can model meaningful
uncertainties and can provide evidence-based clinical support by clustering
similar patients.
