Title: Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks,
Abstract: Viewing the trajectory of a patient as a dynamical system, a recurrent neural
network was developed to learn the course of patient encounters in the
Pediatric Intensive Care Unit (PICU) of a major tertiary care center. Data
extracted from Electronic Medical Records (EMR) of about 12000 patients who
were admitted to the PICU over a period of more than 10 years were leveraged.
The RNN model ingests a sequence of measurements which include physiologic
observations, laboratory results, administered drugs and interventions, and
generates temporally dynamic predictions for in-ICU mortality at user-specified
times. The RNN's ICU mortality predictions offer significant improvements over
those from two clinically-used scores and static machine learning algorithms.