Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury

Traumatic brain injury (TBI) can lead to severe adverse clinical outcomes, including death and disability. Early detection of in-hospital mortality in high-risk populations may enable early treatment and potentially reduce mortality using machine learning. However, there is limited information on in...

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Autores principales:	Sheng-Der Hsu, En Chao, Sy-Jou Chen, Dueng-Yuan Hueng, Hsiang-Yun Lan, Hui-Hsun Chiang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electronic medical record machine learning Glasgow coma scale (GCS) injury severity scale (ISS) blood pressure traumatic brain injury (TBI) Medicine R
Acceso en línea:	https://doaj.org/article/b95ca00222b442da8d7599de820ae4c7
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spelling	oai:doaj.org-article:b95ca00222b442da8d7599de820ae4c72021-11-25T18:07:32ZMachine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury10.3390/jpm111111442075-4426https://doaj.org/article/b95ca00222b442da8d7599de820ae4c72021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4426/11/11/1144https://doaj.org/toc/2075-4426Traumatic brain injury (TBI) can lead to severe adverse clinical outcomes, including death and disability. Early detection of in-hospital mortality in high-risk populations may enable early treatment and potentially reduce mortality using machine learning. However, there is limited information on in-hospital mortality prediction models for TBI patients admitted to emergency departments. The aim of this study was to create a model that successfully predicts, from clinical measures and demographics, in-hospital mortality in a sample of TBI patients admitted to the emergency department. Of the 4881 TBI patients who were screened at the emergency department at a high-level first aid duty hospital in northern Taiwan, 3331 were assigned in triage to Level I or Level II using the Taiwan Triage and Acuity Scale from January 2008 to June 2018. The most significant predictors of in-hospital mortality in TBI patients were the scores on the Glasgow coma scale, the injury severity scale, and systolic blood pressure in the emergency department admission. This study demonstrated the effective cutoff values for clinical measures when using machine learning to predict in-hospital mortality of patients with TBI. The prediction model has the potential to further accelerate the development of innovative care-delivery protocols for high-risk patients.Sheng-Der HsuEn ChaoSy-Jou ChenDueng-Yuan HuengHsiang-Yun LanHui-Hsun ChiangMDPI AGarticleelectronic medical recordmachine learningGlasgow coma scale (GCS)injury severity scale (ISS)blood pressuretraumatic brain injury (TBI)MedicineRENJournal of Personalized Medicine, Vol 11, Iss 1144, p 1144 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electronic medical record machine learning Glasgow coma scale (GCS) injury severity scale (ISS) blood pressure traumatic brain injury (TBI) Medicine R
spellingShingle	electronic medical record machine learning Glasgow coma scale (GCS) injury severity scale (ISS) blood pressure traumatic brain injury (TBI) Medicine R Sheng-Der Hsu En Chao Sy-Jou Chen Dueng-Yuan Hueng Hsiang-Yun Lan Hui-Hsun Chiang Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury
description	Traumatic brain injury (TBI) can lead to severe adverse clinical outcomes, including death and disability. Early detection of in-hospital mortality in high-risk populations may enable early treatment and potentially reduce mortality using machine learning. However, there is limited information on in-hospital mortality prediction models for TBI patients admitted to emergency departments. The aim of this study was to create a model that successfully predicts, from clinical measures and demographics, in-hospital mortality in a sample of TBI patients admitted to the emergency department. Of the 4881 TBI patients who were screened at the emergency department at a high-level first aid duty hospital in northern Taiwan, 3331 were assigned in triage to Level I or Level II using the Taiwan Triage and Acuity Scale from January 2008 to June 2018. The most significant predictors of in-hospital mortality in TBI patients were the scores on the Glasgow coma scale, the injury severity scale, and systolic blood pressure in the emergency department admission. This study demonstrated the effective cutoff values for clinical measures when using machine learning to predict in-hospital mortality of patients with TBI. The prediction model has the potential to further accelerate the development of innovative care-delivery protocols for high-risk patients.
format	article
author	Sheng-Der Hsu En Chao Sy-Jou Chen Dueng-Yuan Hueng Hsiang-Yun Lan Hui-Hsun Chiang
author_facet	Sheng-Der Hsu En Chao Sy-Jou Chen Dueng-Yuan Hueng Hsiang-Yun Lan Hui-Hsun Chiang
author_sort	Sheng-Der Hsu
title	Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury
title_short	Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury
title_full	Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury
title_fullStr	Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury
title_full_unstemmed	Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury
title_sort	machine learning algorithms to predict in-hospital mortality in patients with traumatic brain injury
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b95ca00222b442da8d7599de820ae4c7
work_keys_str_mv	AT shengderhsu machinelearningalgorithmstopredictinhospitalmortalityinpatientswithtraumaticbraininjury AT enchao machinelearningalgorithmstopredictinhospitalmortalityinpatientswithtraumaticbraininjury AT syjouchen machinelearningalgorithmstopredictinhospitalmortalityinpatientswithtraumaticbraininjury AT duengyuanhueng machinelearningalgorithmstopredictinhospitalmortalityinpatientswithtraumaticbraininjury AT hsiangyunlan machinelearningalgorithmstopredictinhospitalmortalityinpatientswithtraumaticbraininjury AT huihsunchiang machinelearningalgorithmstopredictinhospitalmortalityinpatientswithtraumaticbraininjury
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Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury

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