  With the improvement of medical data capturing, vast amount of continuous
patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs
and medications, become available for clinical decision support at intensive
care units (ICUs). However, it becomes increasingly challenging to model such
data, due to high density of the monitoring data, heterogeneous data types and
the requirement for interpretable models. Integration of these high-density
monitoring data with the discrete clinical events (including diagnosis,
medications, labs) is challenging but potentially rewarding since richness and
granularity in such multimodal data increase the possibilities for accurate
detection of complex problems and predicting outcomes (e.g., length of stay and
mortality). We propose Recurrent Attentive and Intensive Model (RAIM) for
jointly analyzing continuous monitoring data and discrete clinical events. RAIM
introduces an efficient attention mechanism for continuous monitoring data
(e.g., ECG), which is guided by discrete clinical events (e.g, medication
usage). We apply RAIM in predicting physiological decompensation and length of
stay in those critically ill patients at ICU. With evaluations on MIMIC- III
Waveform Database Matched Subset, we obtain an AUC-ROC score of 90.18% for
predicting decompensation and an accuracy of 86.82% for forecasting length of
stay with our final model, which outperforms our six baseline models.
