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- A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months
- Mehdi Nourelahi, Fardad Dadboud, Hosseinali Khalili, Amin Niakan, Hossein Parsaei
- Acute Crit Care. 2022;37(1):45-52. Published online January 21, 2022
- DOI: https://doi.org/10.4266/acc.2021.00486
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- 9 Web of Science
- 8 Crossref
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Abstract
PDF - Background
Traumatic brain injury (TBI), which occurs commonly worldwide, is among the more costly of health and socioeconomic problems. Accurate prediction of favorable outcomes in severe TBI patients could assist with optimizing treatment procedures, predicting clinical outcomes, and result in substantial economic savings. Methods: In this study, we examined the capability of a machine learning-based model in predicting “favorable” or “unfavorable” outcomes after 6 months in severe TBI patients using only parameters measured on admission. Three models were developed using logistic regression, random forest, and support vector machines trained on parameters recorded from 2,381 severe TBI patients admitted to the neuro-intensive care unit of Rajaee (Emtiaz) Hospital (Shiraz, Iran) between 2015 and 2017. Model performance was evaluated using three indices: sensitivity, specificity, and accuracy. A ten-fold cross-validation method was used to estimate these indices. Results: Overall, the developed models showed excellent performance with the area under the curve around 0.81, sensitivity and specificity of around 0.78. The top-three factors important in predicting 6-month post-trauma survival status in TBI patients are “Glasgow coma scale motor response,” “pupillary reactivity,” and “age.” Conclusions: Machine learning techniques might be used to predict the 6-month outcome in TBI patients using only the parameters measured on admission when the machine learning is trained using a large data set. -
Citations
Citations to this article as recorded by
- Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study
Guangming Zhu, Burak B Ozkara, Hui Chen, Bo Zhou, Bin Jiang, Victoria Y Ding, Max Wintermark
The Neuroradiology Journal.2024; 37(1): 74. CrossRef - Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions
Kevin Pierre, Jordan Turetsky, Abheek Raviprasad, Seyedeh Mehrsa Sadat Razavi, Michael Mathelier, Anjali Patel, Brandon Lucke-Wold
Trauma Care.2024; 4(1): 31. CrossRef - Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care
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Current Opinion in Clinical Nutrition & Metabolic Care.2023; 26(5): 476. CrossRef - Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis
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BMC Medical Informatics and Decision Making.2023;[Epub] CrossRef - Predicting return to work after traumatic brain injury using machine learning and administrative data
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International Journal of Medical Informatics.2023; 178: 105201. CrossRef - Fluid-Based Protein Biomarkers in Traumatic Brain Injury: The View from the Bedside
Denes V. Agoston, Adel Helmy
International Journal of Molecular Sciences.2023; 24(22): 16267. CrossRef - Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics
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Biomedicines.2022; 10(9): 2267. CrossRef
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