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.
@article{arxiv.1701.06675,
title = {Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks},
author = {M Aczon and D Ledbetter and L Ho and A Gunny and A Flynn and J Williams and R Wetzel},
journal= {arXiv preprint arXiv:1701.06675},
year = {2017}
}