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Cited 5 time in webofscience Cited 7 time in scopus
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A Machine Learning?Based Prognostic Model for the Prediction of Early Death After Traumatic Brain Injury: Comparison with the Corticosteroid Randomization After Significant Head Injury (CRASH) Modelopen access

Authors
Lee, S.H.Lee, C.H.Hwang, S.H.Kang, D.H.
Issue Date
Oct-2022
Publisher
Elsevier Inc.
Keywords
Corticosteroid randomization after significant head injury model; Early death; Machine learning; Prognostic model; Trauma; Traumatic brain injury
Citation
World Neurosurgery, v.166, pp E125 - E134
Indexed
SCIE
SCOPUS
Journal Title
World Neurosurgery
Volume
166
Start Page
E125
End Page
E134
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/819
DOI
10.1016/j.wneu.2022.06.130
ISSN
1878-8750
1878-8769
Abstract
Background: Machine learning (ML) has been used to predict the outcomes of traumatic brain injury. However, few studies have reported the use of ML models to predict early death. This study aimed to develop ML models for early death prediction and to compare performance with the corticosteroid randomization after significant head injury (CRASH) model. Methods: We retrospectively reviewed traumatic brain injury patients between February 2017 and August 2021. The patients were randomly assigned to a training set and a test set. Predictive variables included clinical findings, laboratory values, and computed tomography findings. The ML models (random forest, support vector machine [SVM], logistic regression) were developed with the training set. The CRASH model is a prognostic model that was developed based on 10,008 patients included in the CRASH trial. The ML and CRASH models were applied to the test set to evaluate the performance. Results: A total of 423 patients were included; 317 and 106 patients were randomly assigned to the training and test sets, respectively. The area under the curve was highest in the SVM (0.952, 95% confidence interval = 0.906?0.990) and lowest in the CRASH model (0.942, 95% confidence interval = 0.886?0.999). There were no significant differences between the area under the curves of the ML and CRASH models (P = 0.899 for random forest vs. the CRASH model, P = 0.760 for SVM vs. the CRASH model, P = 0.806 for logistic regression vs. the CRASH model). Conclusions: The ML models may have comparable performances compared to the CRASH model despite being developed with a smaller sample size. ? 2022 Elsevier Inc.
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