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

arXiv: https://arxiv.org/abs/1701.06675 · HTML: https://mlsanity.com/paper/1701.06675/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2017.

## Built on by (most cited first, 2 of 2)

- [Heterogeneous Similarity Graph Neural Network on Electronic Health Records](https://mlsanity.com/paper/2101.06800/) (2021)
- [Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System](https://mlsanity.com/paper/2108.10818/) (2021)

## Abstract (arXiv)

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.