Key result
The Classification Tree model predicted pediatric emergency department return visits within 120 hours with 81% accuracy, 79.8% sensitivity, and 97% specificity, outperforming the Artificial Neural Network in sensitivity.
Why the study?
Does a classification tree model improve the prediction of early emergency department return visits in children compared to an artificial neural network?
Observational (n=28,341)
No
Does a classification tree model improve the prediction of early emergency department return visits in children compared to an artificial neural network?
A classification tree model provides high sensitivity and specificity for predicting early return visits to the pediatric emergency department, outperforming artificial neural networks in sensitivity.
This is the authors' abstract. We don't add key points for this paper.
OBJECTIVES: Return visit (RV) to the emergency department (ED) is considered a benchmarking clinical indicator for health care quality. The purpose of this study was to develop a predictive model for early readmission risk in pediatric EDs comparing the performances of 2 learning machine algorithms. METHODS: A retrospective study based on all children younger than 15 years spontaneously returning within 120 hours after discharge was conducted in an Italian university children's hospital between October 2012 and April 2013. Two predictive models, artificial neural network (ANN) and classification tree (CT), were used. Accuracy, specificity, and sensitivity were assessed. RESULTS: A total of 28,341 patient records were evaluated. Among them, 626 patients returned to the ED within 120 hours after their initial visit. Comparing ANN and CT, our analysis has shown that CT is the best model to predict RVs. The CT model showed an overall accuracy of 81%, slightly lower than the one achieved by the ANN (91.3%), but CT outperformed ANN with regard to sensitivity (79.8% vs 6.9%, respectively). The specificity was similar for the 2 models (CT, 97% vs ANN, 98.3%). In addition, the time of arrival and discharge along with the priority code assigned in triage, age, and diagnosis play a pivotal role to identify patients at high risk of RVs. CONCLUSIONS: These models provide a promising predictive tool for supporting the ED staff in preventing unnecessary RVs.
Classification tree outperformed neural networks for predicting early pediatric ED returns; hypothesis-generating and requires prospective validation before use.
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Frigerio et al. (2016) conducted an observational in Pediatric Emergency Department visits (n=28,341). Classification Tree (CT) and Artificial Neural Network (ANN) predictive models was evaluated on Accuracy, sensitivity, and specificity in predicting return visits within 120 hours. The Classification Tree model predicted pediatric emergency department return visits within 120 hours with 81% accuracy, 79.8% sensitivity, and 97% specificity, outperforming the Artificial Neural Network in sensitivity.
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