Julia Elrod1,2, Christoph Mohr1, Ruben Wolff3, Michael Boettcher1,2, Konrad Reinshagen1,2, Pia Bartels1, Ingo Koenigs1,2. 1. Department of Paediatric Surgery, University Medical Centre Eppendorf, Hamburg, Germany. 2. Burn Unit, Plastic and Reconstructive Surgery, Department of Paediatric Surgery, Altona Children's Hospital, Hamburg, Germany. 3. Neoglia LTD, London, United Kingdom.
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.
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.