Continuous Prediction of Mortality in the PICU: A Recurrent Neural Network Model in a Single-Center Dataset*

OBJECTIVES: Develop, as a proof of concept, a recurrent neural network model using electronic medical records data capable of continuously assessing an individual child’s risk of mortality throughout their ICU stay as a proxy measure of severity of illness. DESIGN: Retrospective cohort study. SETTING: PICU in a tertiary care academic children’s hospital. PATIENTS/SUBJECTS: Twelve thousand five hundred sixteen episodes (9,070 children) admitted to the PICU between January 2010 and February 2019, partitioned into training (50%), validation (25%), and test (25%) sets. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: On 2,475 test set episodes lasting greater than or equal to 24 hours in the PICU, the area under the receiver operating characteristic curve of the recurrent neural network’s 12th hour predictions was 0.94 (CI, 0.93–0.95), higher than those of Pediatric Index of Mortality 2 (0.88; CI, [0.85–0.91]; p
Source: Pediatric Critical Care Medicine - Category: Pediatrics Tags: Feature Articles Source Type: research

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