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

2017/01/23 by Melissa Aczon, David Ledbetter, Aczon, M +11 · 1 citation
Computer Science · Medicine · #Dynamical Systems (math.DS) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Mathematics #Healthcare Technology and Patient Monitoring #Machine Learning (stat.ML) #Machine Learning in Healthcare #Neural and Evolutionary Computing (cs.NE) #Quantitative Methods (q-bio.QM) #Sepsis Diagnosis and Treatment

paper · pdf · doi:10.48550/arxiv.1701.06675

openalex publication_date 2017/01/23 · openalex created_date 2017/02/03 · openalex updated_date 2026/07/28

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.

Citations

• Long Short-Term Memory
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