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Viewing the trajectory of a patient as a dynamical system, a recurrent neuralnetwork was developed to learn the course of patient encounters in thePediatric Intensive Care Unit (PICU) of a major tertiary care center. Dataextracted from Electronic Medical Records (EMR) of about 12000 patients whowere admitted to the PICU over a period of more than 10 years were leveraged.The RNN model ingests a sequence of measurements which include physiologicobservations, laboratory results, administered drugs and interventions, andgenerates temporally dynamic predictions for in-ICU mortality at user-specifiedtimes. The RNN's ICU mortality predictions offer significant improvements overthose from two clinically-used scores and static machine learning algorithms.

Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks