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

Abstract

The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

“…To the best of our knowledge, there is no similar evidence on the pediatric population with HF, even if MLTs have also been primarily used in developing predictive models in critically ill children. For example, several studies used MLTs for early mortality prediction, the development of sepsis, or the need for pediatric intensive care unit transfer for newly hospitalized children [ 33 , 34 , 35 ]. However, no studies have investigated the development of a severe condition such as HF in patients admitted to PICU.…”

“…Notably, we found that the most important variables in the random forest model have also been shown to be important predictors in prior research in pediatric settings [ 12 ]. Recently, Lee et al developed a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission and compared it with PIM 3 score [ 33 ]. Interestingly, they found that the random forest model performed better than PIM 3 score in predicting mortality.…”

Machine Learning-Based Systems for the Anticipation of Adverse Events After Pediatric Cardiac Surgery

“…Most of the proposed models were developed to make one prediction per patient encounter using data within the first hours after ICU admission (37)(38)(39)(40), rather than predict the risk of mortality continuously across the entire encounter. However, in order to continually assess individual patient's risk of clinical deterioration or mortality it is important to integrate information not only from a single time point, as the current scoring systems do, but also data from previous time points, that is, longitudinal temporal data.…”

An Artificial Neural Network Model for Pediatric Mortality Prediction in Two Tertiary Pediatric Intensive Care Units in South Africa. A Development Study

“…Advanced machine learning algorithms offer an ability to engage with complexity and non-linearity that make them an appealing tool for a wide range of new applications such as image recognition with convolutional neural networks (20) and time series analysis with recurrent neural networks (21) and existing applications such as prediction of end-points such as mortality (22). Recently, several studies have reported the use of machine learning approaches with encouraging results (21,(23)(24)(25). To date, the full extent to which machine learning approaches can be applied in pediatric research has not been fully explored, but, as Londsdale et al point out, the opportunities for improving patient care are substantial (26).…”

“…Artificial neural networks used in supervised classification tasks such as described in this study are referred to as perceptrons. Weights are adjusted through stochastic gradient descent (25).…”