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Fardad Dadboud 1 Article
Basic science and research
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
AbstractAbstract 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
    Olivia F. Hunter, Frances Perry, Mina Salehi, Hubert Bandurski, Alan Hubbard, Chad G. Ball, S. Morad Hameed
    World Journal of Emergency Surgery.2023;[Epub]     CrossRef
  • Gastrointestinal failure, big data and intensive care
    Pierre Singer, Eyal Robinson, Orit Raphaeli
    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
    Jue Wang, Ming Jing Yin, Han Chun Wen
    BMC Medical Informatics and Decision Making.2023;[Epub]     CrossRef
  • Predicting return to work after traumatic brain injury using machine learning and administrative data
    Helena Van Deynse, Wilfried Cools, Viktor-Jan De Deken, Bart Depreitere, Ives Hubloue, Eva Kimpe, Maarten Moens, Karen Pien, Ellen Tisseghem, Griet Van Belleghem, Koen Putman
    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
    Antonio Cerasa, Gennaro Tartarisco, Roberta Bruschetta, Irene Ciancarelli, Giovanni Morone, Rocco Salvatore Calabrò, Giovanni Pioggia, Paolo Tonin, Marco Iosa
    Biomedicines.2022; 10(9): 2267.     CrossRef

ACC : Acute and Critical Care