Using Artificial Intelligence to Obtain More Evidence? Prediction of Length of Hospitalization in Pediatric Burn Patients

Abstract

Background: It is not only important for counseling purposes and for healthcare management. This study investigates the prediction accuracy of an artificial intelligence (AI)-based approach and a linear model. The heuristic expecting 1 day of stay per percentage of total body surface area (TBSA) serves as the performance benchmark.Methods: The study is based on pediatric burn patient's data sets from an international burn registry (N = 8,542). Mean absolute error and standard error are calculated for each prediction model (rule of thumb, linear regression, and random forest). Factors contributing to a prolonged stay and the relationship between TBSA and the residual error are analyzed.Results: The random forest-based approach and the linear model are statistically superior to the rule of thumb (p < 0.001, resp. p = 0.009). The residual error rises as TBSA increases for all methods. Factors associated with a prolonged LOS are particularly TBSA, depth of burn, and inhalation trauma.Conclusion: Applying AI-based algorithms to data from large international registries constitutes a promising tool for the purpose of prediction in medicine in the future; however, certain prerequisites concerning the underlying data sets and certain shortcomings must be considered.

Predicting the behavioral intentions of hospice and palliative care providers from real-world data using supervised learning: A cross-sectional survey study

“…Each tree node divides the data into two subsets, thus making each subset more homogeneous (Figure 1). The terminal node (leaf) of the tree that a certain data point falls into determines the predictions by the majority selection of the training data (30). RF is a meta-estimator that fits several DT classifiers on various subsamples of the dataset and uses averaging to improve the predictive accuracy and control overfitting (31).…”