Title: Deep Learning to Attend to Risk in ICU,
Abstract: Modeling physiological time-series in ICU is of high clinical importance.
However, data collected within ICU are irregular in time and often contain
missing measurements. Since absence of a measure would signify its lack of
importance, the missingness is indeed informative and might reflect the
decision making by the clinician. Here we propose a deep learning architecture
that can effectively handle these challenges for predicting ICU mortality
outcomes. The model is based on Long Short-Term Memory, and has layered
attention mechanisms. At the sensing layer, the model decides whether to
observe and incorporate parts of the current measurements. At the reasoning
layer, evidences across time steps are weighted and combined. The model is
evaluated on the PhysioNet 2012 dataset showing competitive and interpretable
results.