Application of machine learning to predict the outcome of pediatric traumatic brain injury

Purpose: Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI. Met...

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Autores principales:	Thara Tunthanathip, Thakul Oearsakul
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Pediatrics Traumatic brain injury Machine learning Support vector machine Random forest Logistic regression Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/8206904b5cd948df92003fa45753e150
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spelling	oai:doaj.org-article:8206904b5cd948df92003fa45753e1502021-11-18T04:43:43ZApplication of machine learning to predict the outcome of pediatric traumatic brain injury1008-127510.1016/j.cjtee.2021.06.003https://doaj.org/article/8206904b5cd948df92003fa45753e1502021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1008127521000985https://doaj.org/toc/1008-1275Purpose: Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI. Methods: A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets. Results: There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2–179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78. Conclusion: The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.Thara TunthanathipThakul OearsakulElsevierarticlePediatricsTraumatic brain injuryMachine learningSupport vector machineRandom forestLogistic regressionMedicine (General)R5-920ENChinese Journal of Traumatology, Vol 24, Iss 6, Pp 350-355 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Pediatrics Traumatic brain injury Machine learning Support vector machine Random forest Logistic regression Medicine (General) R5-920
spellingShingle	Pediatrics Traumatic brain injury Machine learning Support vector machine Random forest Logistic regression Medicine (General) R5-920 Thara Tunthanathip Thakul Oearsakul Application of machine learning to predict the outcome of pediatric traumatic brain injury
description	Purpose: Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI. Methods: A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets. Results: There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2–179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78. Conclusion: The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.
format	article
author	Thara Tunthanathip Thakul Oearsakul
author_facet	Thara Tunthanathip Thakul Oearsakul
author_sort	Thara Tunthanathip
title	Application of machine learning to predict the outcome of pediatric traumatic brain injury
title_short	Application of machine learning to predict the outcome of pediatric traumatic brain injury
title_full	Application of machine learning to predict the outcome of pediatric traumatic brain injury
title_fullStr	Application of machine learning to predict the outcome of pediatric traumatic brain injury
title_full_unstemmed	Application of machine learning to predict the outcome of pediatric traumatic brain injury
title_sort	application of machine learning to predict the outcome of pediatric traumatic brain injury
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/8206904b5cd948df92003fa45753e150
work_keys_str_mv	AT tharatunthanathip applicationofmachinelearningtopredicttheoutcomeofpediatrictraumaticbraininjury AT thakuloearsakul applicationofmachinelearningtopredicttheoutcomeofpediatrictraumaticbraininjury
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Application of machine learning to predict the outcome of pediatric traumatic brain injury

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