  In healthcare applications, temporal variables that encode movement, health
status and longitudinal patient evolution are often accompanied by rich
structured information such as demographics, diagnostics and medical exam data.
However, current methods do not jointly optimize over structured covariates and
time series in the feature extraction process. We present ShortFuse, a method
that boosts the accuracy of deep learning models for time series by explicitly
modeling temporal interactions and dependencies with structured covariates.
ShortFuse introduces hybrid convolutional and LSTM cells that incorporate the
covariates via weights that are shared across the temporal domain. ShortFuse
outperforms competing models by 3% on two biomedical applications, forecasting
osteoarthritis-related cartilage degeneration and predicting surgical outcomes
for cerebral palsy patients, matching or exceeding the accuracy of models that
use features engineered by domain experts.
