A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage

Abstract

Objectives: Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization.Earlier identification of need for hospital-level care could triage patients more efficiently to high-or low-resource ED tracks. Existing tools to predict disposition for pediatric asthma use only clinical data, perform best several hours into the ED stay, and are static or score-based. Machine learning offers a population-specific, dynamic option that allows real-time integration of available nonclinical data at triage. Our objective was to compare the performance of four common machine learning approaches, incorporating clinical data available at the time of triage with information about weather, neighborhood characteristics, and community viral load for early prediction of the need for hospital-level care in pediatric asthma.Methods: Retrospective analysis of patients ages 2 to 18 years seen at two urban pediatric EDs with asthma exacerbation over 4 years. Asthma exacerbation was defined as receiving both albuterol and systemic corticosteroids. We included patient features, measures of illness severity available in triage, weather features, and Centers for Disease Control and Prevention influenza patterns. We tested four models: decision trees, LASSO logistic regression, random forests, and gradient boosting machines. For each model, 80% of the data set was used for training and 20% was used to validate the models. The area under the receiver operating characteristic (AUC) curve was calculated for each model.Results: There were 29,392 patients included in the analyses: mean (AESD) age of 7.0 (AE4.2) years, 42% female, 77% non-Hispanic black, and 76% public insurance. The AUCs for each model were: decision tree 0.72 (95% confidence interval [CI] = 0.66-0.77), logistic regression 0.83 (95% CI = 0.82-0.83), random forests 0.82 (95% CI = 0.81-0.83), and gradient boosting machines 0.84 (95% CI = 0.83-0.85). In the lowest decile of risk, only 3% of patients required hospitalization; in the highest decile this rate was 100%. After patient vital signs and acuity, age and weight, followed by socioeconomic status (SES) and weather-related features, were the most important for predicting hospitalization.Conclusions: Three of the four machine learning models performed well with decision trees preforming the worst. The gradient boosting machines model demonstrated a slight advantage over other approaches at predicting need for hospital-level care at the time of triage in pediatric patients presenting with asthma exacerbation. The addition of weight, SES, and weather data improved the performance of this model.

Artificial intelligence in emergency medicine: A scoping review

“…For example, supervised ML interventions included prediction models for pediatric asthma exacerbation, prediction of return visits, and stroke diagnosis. [19][20][21] NLP models were used to optimize resource allocation in low-resource settings, classify computed tomography (CT) imaging, and predict hospital admission using electronic medical records (EMR). [22][23][24] There also appears to be rapidly growing interest in the varied opportunities for AI, as most studies were published in the last 5 years.…”

“…Overall, we found that AI interventions in the ED are heterogeneous in both purpose and design. For example, supervised ML interventions included prediction models for pediatric asthma exacerbation, prediction of return visits, and stroke diagnosis 19–21 . NLP models were used to optimize resource allocation in low‐resource settings, classify computed tomography (CT) imaging, and predict hospital admission using electronic medical records (EMR) 22–24 .…”

A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study

“…In a real-world validation study, the HAI system improved the accuracy of hip fracture diagnosis to 97%, with a false-negative rate of 0.65%. Several reports have shown that the algorithm might help physicians in acute care and could save lives [10][11][12][13][14][15].…”

“…Several studies have shown the possibility of using algorithms trained with a large amount of data to aid in appropriate triage, accurately predicting outcomes, improving diagnoses and referrals in clinical situations, and even shortening the waiting time for reports [10][11][12][13][14][15]. An increasing amount of supporting evidence shows that the use of computer vision with deep neural networks-a rapidly advancing technology ideally suited to solving image-based problems-achieves excellent performance, comparable to that of experts [12][13][14][15][16][17][18].…”

Asthma

“…Consider education or electronic decision support as interventions for a quality improvement project to reduce disparities in care. Use machine learning to analyze large data sets (eg, all ED visits, [ 19 ] asthma registry) to identify associations between risk factors and poor asthma outcomes previously not considered. Collaborate with local resources to mitigate risk (legal aid to assist with poor housing, care coordination to ensure medications prescribed are covered and there is subspecialty follow-up).…”