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

• Main Authors: Julia Elrod (Author), Christoph Mohr (Author), Ruben Wolff (Author), Michael Boettcher (Author), Konrad Reinshagen (Author), Pia Bartels (Author), German Burn Registry (Author), Ingo Koenigs (Author)
• Format: Book
• Published: Frontiers Media S.A., 2021-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2296-2360; 10.3389/fped.2020.613736
• Keywords: artificial intelligence; burns; length of hospitalization; prediction; accuracy; paediatric; Pediatrics; RJ1-570
• Source: Frontiers in Pediatrics, Vol 8 (2021)

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.