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

ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information