  Dynamic assessment of patient status (e.g. by an automated, continuously
updated assessment of outcome) in the Intensive Care Unit (ICU) is of paramount
importance for early alerting, decision support and resource allocation.
Extraction and cleaning of expert-selected clinical variables discards
information and protracts collaborative efforts to introduce machine learning
in medicine. We present improved aggregation methods for a flexible deep
learning architecture which learns a joint representation of patient chart, lab
and output events. Our models outperform recent deep learning models for
patient mortality classification using ICU timeseries, by embedding and
aggregating all events with no pre-processing or variable selection. Our model
achieves a strong performance of AUROC 0.87 at 48 hours on the MIMIC-III
dataset while using 13,233 unique un-preprocessed variables in an interpretable
manner via hourly softmax aggregation. This demonstrates how our method can be
easily combined with existing electronic health record systems for automated,
dynamic patient risk analysis.

Machine Learning;Machine Learning