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

Abstract

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. Electronic supplementary material The online version of this article (10.1186/s13054-019-2561-z) contains supplementary material, which is available to authorized users.

Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

“…Thus far, there has been only one study predicting mortality in children using machine learning. The retrospective cohort study developed a model that predicts mortality after 6–60 h of ICU admission by learning the vital signs trend of the a ‘24-h window’ in a convolutional neural network 10 . However, since it is necessary to analyze the 24-h window, it is difficult to predict results up to 24 h after ICU admission.…”

Change in Age Distribution of the Injuries and Comparison of Different Scoring Tools in Predicting Mortality Among Geriatric Trauma Patients

“…On this basis, the popularization of arti cial intelligence and deep learning algorithms have promoted the accurate prediction of trauma patients' clinical outcomes. It has been pointed out that the advanced deep learning algorithm can better predict the adverse outcomes of critically patients than the traditional regression algorithm [57,58].…”

Artificial Intelligence in the Intensive Care Unit

“…A Swedish system using artificial neural networks applied to >200,000 first-time ICU admissions also showed superior performance in predicting the risk of dying when compared to SAPS-3 [24]. Machine learning models have also been proposed to predict mortality in trauma [25] and pediatric ICU patients [26].…”