AUTHOR=Elrod Julia , Mohr Christoph , Wolff Ruben , Boettcher Michael , Reinshagen Konrad , Bartels Pia ,  German Burn Registry , Koenigs Ingo 

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

JOURNAL=Frontiers in Pediatrics

VOLUME=Volume 8 - 2020

YEAR=2021

URL=https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2020.613736

DOI=10.3389/fped.2020.613736

ISSN=2296-2360

ABSTRACT=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.