Using Artificial Intelligence to Obtain More Evidence? Prediction of Length of Hospitalization in Pediatric Burn Patients
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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; Koenigs, Ingo; German Burn Registry.
In: FRONT PEDIATR, Vol. 8, 2020, p. 613736.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Using Artificial Intelligence to Obtain More Evidence? Prediction of Length of Hospitalization in Pediatric Burn Patients
AU - Elrod, Julia
AU - Mohr, Christoph
AU - Wolff, Ruben
AU - Boettcher, Michael
AU - Reinshagen, Konrad
AU - Bartels, Pia
AU - Koenigs, Ingo
AU - German Burn Registry
N1 - Copyright © 2021 Elrod, Mohr, Wolff, Boettcher, Reinshagen, Bartels, German Burn Registry and Koenigs.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
U2 - 10.3389/fped.2020.613736
DO - 10.3389/fped.2020.613736
M3 - SCORING: Journal article
C2 - 33537267
VL - 8
SP - 613736
JO - FRONT PEDIATR
JF - FRONT PEDIATR
SN - 2296-2360
ER -
