Journal Article

A deep learning model for real-time mortality prediction in critically ill children

Kim, Yoon Hee, Kim, Soo Yeon, Park, Minseop, Kim, Young Suh, Sol, In Suk, Kim, Saehoon, Cho, Inhyeok, Jang, Haerin, Cho, Joongbum, Sung, Youngchul, Sohn, Myung Hyun, Kim, Kyung Won

2019

Overview

Background The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortality Prediction Tool (PROMPT), for real-time prediction of all-cause mortality in pediatric intensive care units. Methods Utilizing two separate retrospective observational cohorts, we conducted model development and validation using a machine learning algorithm with a convolutional neural network. The development cohort comprised 1445 pediatric patients with 1977 medical encounters admitted to intensive care units from January 2011 to December 2017 at Severance Hospital (Seoul, Korea). The validation cohort included 278 patients with 364 medical encounters admitted to the pediatric intensive care unit from January 2016 to November 2017 at Samsung Medical Center. Results Using seven vital signs, along with patient age and body weight on intensive care unit admission, PROMPT achieved an area under the receiver operating characteristic curve in the range of 0.89–0.97 for mortality prediction 6 to 60 h prior to death. Our results demonstrated that PROMPT provided high sensitivity with specificity and outperformed the conventional severity scoring system, the Pediatric Index of Mortality, in predictive ability. Model performance was indistinguishable between the development and validation cohorts. Conclusions PROMPT is a deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients.

• Publisher: BioMed Central,BioMed Central Ltd,BMC
• Subject: Adolescent / Algorithms / Area Under Curve / Artificial neural networks / Big Data / Body weight / Child / Child health / Child, Preschool / Children / Cohort Studies / Critical Care Medicine / Critical Illness - mortality / Critically ill children / Data mining / Deep Learning / Emergency Medicine / Female / Humans / Infant / Intensive / Intensive care units, pediatric / Machine learning / Male / Medicine / Medicine & Public Health / Mortality / Mortality - trends / Patient outcomes / Pediatric intensive care / Pediatrics / Pediatrics - instrumentation / Pediatrics - methods / Pediatrics - standards / Prognosis / Republic of Korea / Retrospective Studies / Risk assessment / Risk Assessment - methods / ROC Curve / South Korea