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Modeling physiological time-series in ICU is of high clinical importance.However, data collected within ICU are irregular in time and often containmissing measurements. Since absence of a measure would signify its lack ofimportance, the missingness is indeed informative and might reflect thedecision making by the clinician. Here we propose a deep learning architecturethat can effectively handle these challenges for predicting ICU mortalityoutcomes. The model is based on Long Short-Term Memory, and has layeredattention mechanisms. At the sensing layer, the model decides whether toobserve and incorporate parts of the current measurements. At the reasoninglayer, evidences across time steps are weighted and combined. The model isevaluated on the PhysioNet 2012 dataset showing competitive and interpretableresults.

Deep Learning to Attend to Risk in ICU