Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

Abstract

Key Points Question Do machine learning approaches improve the ability to predict clinical outcomes and disposition of children at emergency department triage? Findings In this prognostic study of a nationally representative sample of 52 037 emergency department visits by children, machine learning–based triage models had better discrimination ability for clinical outcomes and disposition compared with the conventional triage approaches, with a higher sensitivity for the critical care outcome and higher specificity for the hospitalization outcome. Meaning Machine learning may improve the prediction ability of triage approaches and could be used to reduce undertriage of critically ill children and to improve resource allocation in emergency departments.

Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

“…That such a model can accurately predict hospitalization outcome suggests that SISA/SISAL could be expanded to undifferentiated febrile illness. The ability of machine learning models to predict hospital admission outcomes using only emergency department triage data lends support to expanding our approach to undifferentiated fever [34,35]. Of the suspected arboviral cases analyzed here, approximately 54% were confirmed as acute or recent DENV infection, 17% had acute CHIKV infection, and 29% were negative for DENV, CHIKV or ZIKV (based on analysis of subjects in 2014 and 2015) [1].…”

“…Clinical applications of machine learning for arboviral illnesses, specifically, have included analysis of patient genomes for dengue prognosis [29], scanning of patient sera for DENV [30] or Zika diagnosis [31], thermal image scanning for detection of hemodynamic shock [32], analysis of body temperature patterns for diagnosis of undifferentiated fever etiology [33], and analysis of patient data for dengue fever diagnosis [27]. No studies have yet attempted to use machine learning for prediction of hospitalization among arboviral illness or undifferentiated fever patients, although it has been used to predict critical care and hospitalization outcomes based on emergency department triage data in children and adults [34,35].…”

Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania

“…The variable importance is a scaled measure with a maximum value of 100. 17 The R code used for this analysis is shown in online supplemental material 1.…”

Predicting Anticoagulation Need for Otogenic Intracranial Sinus Thrombosis: A Machine Learning Approach

“…Random forest has been validated in the realm of clinical research. 60 We synthesized this machine learning method with meta-regression to investigate our research question and account for missing data, study heterogeneity, and study bias. The purpose of meta-regression is to identify moderator influence on effect size.…”

“…63 The random forest algorithm then builds predictive classification decision trees from each bootstrap sample. The results of each individual tree are aggregated (termed bootstrap aggregating or bagging 60,62,63 ) to yield final predictions, which represent the classes with the greatest proportion of corresponding individual decision trees. In our study, random weights were assigned to random forests to increase the influence of higher quality (i.e., more precise) studies on predictive model building.…”