Using Artificial Intelligence to Obtain More Evidence? Prediction of Length of Hospitalization in Pediatric Burn Patients - Elrod, Julia; Mohr, Christoph; Wolff, Ruben; Boettcher, Michael; Reinshagen, Konrad; Bartels, Pia; , German Burn Registry; Koenigs, Ingo (Frontiers in Pediatrics, 2021)
BACKGROUND: Prediction of length of stay (LOS) in burn patients is important for counselling 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 %total body surface area (TBSA) serves as the performance benchmark. METHODS: The study is based on paediatric burn patient`s data sets from an international burn registry (N=8542). Mean absolute 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 analysed. 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.