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Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach
OBJECTIVES: We aimed to build a machine learning predictive model to predict the risk of prolonged mechanical ventilation (PMV) for patients with Traumatic Brain Injury (TBI). METHODS: This study included TBI patients who were hospitalized in a level 1 trauma center between January 2014 and February...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7343348/ https://www.ncbi.nlm.nih.gov/pubmed/32639971 http://dx.doi.org/10.1371/journal.pone.0235231 |
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author | Abujaber, Ahmad Fadlalla, Adam Gammoh, Diala Abdelrahman, Husham Mollazehi, Monira El-Menyar, Ayman |
author_facet | Abujaber, Ahmad Fadlalla, Adam Gammoh, Diala Abdelrahman, Husham Mollazehi, Monira El-Menyar, Ayman |
author_sort | Abujaber, Ahmad |
collection | PubMed |
description | OBJECTIVES: We aimed to build a machine learning predictive model to predict the risk of prolonged mechanical ventilation (PMV) for patients with Traumatic Brain Injury (TBI). METHODS: This study included TBI patients who were hospitalized in a level 1 trauma center between January 2014 and February 2019. Data were analyzed for all adult patients who received mechanical ventilation following TBI with abbreviated injury severity (AIS) score for the head region of ≥ 3. This study designed three sets of machine learning models: set A defined PMV to be greater than 7 days, set B (PMV > 10 days) and set C (PMV >14 days) to determine the optimal model for deployment. Patients’ demographics, injury characteristics and CT findings were used as predictors. Logistic regression (LR), Artificial neural networks (ANN) Support vector machines (SVM), Random Forest (RF) and C.5 Decision Tree (C.5 DT) were used to predict the PMV. RESULTS: The number of eligible patients that were included in the study were 674, 643 and 622 patients in sets A, B and C respectively. In set A, LR achieved the optimal performance with accuracy 0.75 and Area under the curve (AUC) 0.83. SVM achieved the optimal performance among other models in sets B with accuracy/AUC of 0.79/0.84 respectively. ANNs achieved the optimal performance in set C with accuracy/AUC of 0.76/0.72 respectively. Machine learning models in set B demonstrated more stable performance with higher prediction success and discrimination power. CONCLUSION: This study not only provides evidence that machine learning methods outperform the traditional multivariate analytical methods, but also provides a perspective to reach a consensual definition of PMV. |
format | Online Article Text |
id | pubmed-7343348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-73433482020-07-17 Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach Abujaber, Ahmad Fadlalla, Adam Gammoh, Diala Abdelrahman, Husham Mollazehi, Monira El-Menyar, Ayman PLoS One Research Article OBJECTIVES: We aimed to build a machine learning predictive model to predict the risk of prolonged mechanical ventilation (PMV) for patients with Traumatic Brain Injury (TBI). METHODS: This study included TBI patients who were hospitalized in a level 1 trauma center between January 2014 and February 2019. Data were analyzed for all adult patients who received mechanical ventilation following TBI with abbreviated injury severity (AIS) score for the head region of ≥ 3. This study designed three sets of machine learning models: set A defined PMV to be greater than 7 days, set B (PMV > 10 days) and set C (PMV >14 days) to determine the optimal model for deployment. Patients’ demographics, injury characteristics and CT findings were used as predictors. Logistic regression (LR), Artificial neural networks (ANN) Support vector machines (SVM), Random Forest (RF) and C.5 Decision Tree (C.5 DT) were used to predict the PMV. RESULTS: The number of eligible patients that were included in the study were 674, 643 and 622 patients in sets A, B and C respectively. In set A, LR achieved the optimal performance with accuracy 0.75 and Area under the curve (AUC) 0.83. SVM achieved the optimal performance among other models in sets B with accuracy/AUC of 0.79/0.84 respectively. ANNs achieved the optimal performance in set C with accuracy/AUC of 0.76/0.72 respectively. Machine learning models in set B demonstrated more stable performance with higher prediction success and discrimination power. CONCLUSION: This study not only provides evidence that machine learning methods outperform the traditional multivariate analytical methods, but also provides a perspective to reach a consensual definition of PMV. Public Library of Science 2020-07-08 /pmc/articles/PMC7343348/ /pubmed/32639971 http://dx.doi.org/10.1371/journal.pone.0235231 Text en © 2020 Abujaber et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Abujaber, Ahmad Fadlalla, Adam Gammoh, Diala Abdelrahman, Husham Mollazehi, Monira El-Menyar, Ayman Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach |
title | Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach |
title_full | Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach |
title_fullStr | Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach |
title_full_unstemmed | Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach |
title_short | Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach |
title_sort | using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: machine learning approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7343348/ https://www.ncbi.nlm.nih.gov/pubmed/32639971 http://dx.doi.org/10.1371/journal.pone.0235231 |
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