Skip Navigation
Skip to contents

ACC : Acute and Critical Care

OPEN ACCESS
SEARCH
Search
Advanced Search
mobile menu button

Search

Page Path
HOME > Search
1 "Hosseinali Khalili"
Filter
Filter
Article category
Publication year
Original 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
  • 12,255 View
  • 311 Download
  • 17 Web of Science
  • 19 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  
  • THE USE OF ARTIFICIAL INTELLIGENCE IN TRAUMATOLOGY: A SYSTEMATIC REVIEW AND RECOMMENDATIONS FOR CLINICAL PRACTICE
    V. V. Savgachev, L. B. Shubin
    Bulletin of Pirogov National Medical & Surgical Center.2026; 21(1): 127.     CrossRef
  • The use of machine learning approaches for mortality prediction and classification in traumatic brain injury: a literature review
    ARTHUR AFONSO SILVA-SOUSA, FERNANDA CAROLINA CAMARGO, LEOPOLDO MANDIC F. FURTADO, ANTONIO LUCIO TEIXEIRA, RAFAEL A.B. DE QUEIROZ, ALINE SILVA DE MIRANDA
    Anais da Academia Brasileira de Ciências.2026;[Epub]     CrossRef
  • Development of web- and mobile-based shared decision-making tools in the neurological intensive care unit
    Winnie L. Liu, Lidan Zhang, Soussan Djamasbi, Bengisu Tulu, Susanne Muehlschlegel
    Neurotherapeutics.2025; 22(1): e00503.     CrossRef
  • Long-term survival prediction in patients with acute brain lesions using ensemble machine learning algorithms: a cohort study with combined national health insurance service and its self-run hospital database
    Dougho Park, Daeyoung Hong, Suntak Jin, Jong Hun Kim, Hyoung Seop Kim
    Journal of Big Data.2025;[Epub]     CrossRef
  • Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review
    Armaan K. Malhotra, Husain Shakil, Christopher W. Smith, Yu Qing Huang, Jethro C. C. Kwong, Kevin E. Thorpe, Christopher D. Witiw, Abhaya V. Kulkarni, Jefferson R. Wilson, Avery B. Nathens
    npj Digital Medicine.2025;[Epub]     CrossRef
  • Artificial intelligence in traumatic brain injury: Brain imaging analysis and outcome prediction: A mini review
    Luca Marino, Federico Bilotta
    World Journal of Critical Care Medicine.2025;[Epub]     CrossRef
  • Prediction of Clinically Significant Improvements During the Interdisciplinary Intensive Outpatient Program for Traumatic Brain Injury Using Machine Learning
    Rujirutana Srikanchana, David Samuel, Jacob Powell, Treven Pickett, Thomas DeGraba, Chandler Sours Rhodes
    Annals of Biomedical Engineering.2025; 53(11): 2845.     CrossRef
  • A practical approach to predicting long-term outcomes in traumatic brain injury: Enhancing clinical decision-making with machine learning
    Amirmohammad Farrokhi, Mahtab Jalali, Mohamed Sobhi Jabal, Saeed Abdollahifard, Reza Taheri, Omid Yousefi, Amin Niakan, Hosseinali Khalili
    Computers in Biology and Medicine.2025; 196: 110827.     CrossRef
  • The effect of extended early rehabilitation on the treatment outcome of patients with moderate and severe traumatic brain injury
    Nataša Keleman, Dragana Dragičević-Cvjetković, Aleksandra Mikov, Dragomir Radošević, Ðula Ðilvesi, Vladimir Mrđa, Rastislava Krasnik
    Frontiers in Human Neuroscience.2025;[Epub]     CrossRef
  • Artificial Intelligence in Traumatic Brain Injury: A Systematic Review of Prognostic, Diagnostic, and Monitoring Applications
    Anas E Ahmed, Rayan M Alyami, Fatimah H Al Ghazwi, Renad H Hamzi, Nawa K Alshammari, Fawziah M Jali, Abdullah A Al Alduwayh, Thikra M Almujami, Abdullah S Alamri, Jamal A Sabban, Ghadi F Alsum
    Cureus.2025;[Epub]     CrossRef
  • 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
  • A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings
    Mohsen Khosravi, Seyyed Morteza Mojtabaeian, Emine Kübra Dindar Demiray, Burak Sayar
    Health Science Reports.2024;[Epub]     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
© The Korean Society of Critical Care Medicine.
TOP