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

Frontiers in Pediatrics

article

Julia Elrod¹  Christoph Mohr¹  Ruben Wolff³  Michael Boettcher¹  Konrad Reinshagen¹  Pia Bartels¹  German Burn Registry⁴  Ingo Koenigs¹ 

[1] Department of Paediatric Surgery, University Medical Centre Eppendorf;Burn Unit, Department of Paediatric Surgery, Altona Children's Hospital;United Kingdom;German Society for Burn Treatment

关键词: artificial intelligence;    burns;    length of hospitalization;    prediction;    accuracy;    paediatric;   

DOI  :  10.3389/fped.2020.613736

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY