Predicting the Presence of Traumatic Chest Injuries Using Machine Learning Algorithm.
Introduction: Various tools have been developed to determine the priority of radiography in trauma patients. This study aimed to investigate the role of machine learning models in predicting chest injuries following multiple trauma. Methods: We used the database of a comprehensive cross-sectional su...
| Publicado en: | Archives of Academic Emergency Medicine Vol. 13; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Shahid Beheshti University of Medical Sciences
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190500674&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190500674 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26454904 MFMK jtl: Archives of Academic Emergency Medicine issn: 26454904 maglogo: N pubinfo: dt: 2025 vid: 13 iid: 1 pid: 87963 pub: Shahid Beheshti University of Medical Sciences artinfo: ui: 190500674 190500674 190500674 10.22037/aaemj.v13i1.2512 190500674 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Predicting the Presence of Traumatic Chest Injuries Using Machine Learning Algorithm. aug: au: Vazirizadeh-mahabadii, Mohammadhossein Ghaffari Jolfayi, Amir Hosseini, Mostafa Yarahmadi, Mobina Zarei, Hamed Masoodi, Mohsen Sarveazad, Arash Yousefifard, Mahmoud affil: Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran. sug: subj: Thoracic Injuries Machine Learning Algorithms Risk Assessment Multiple Trauma Human Cross Sectional Studies Surveys ROC Curve Decision Trees Support Vector Machine Logistic Regression Convolutional Neural Networks Sensitivity and Specificity Radiography, Thoracic Emergency Medicine Iran Adult Middle Age Male Female Oxygen Saturation Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Introduction: Various tools have been developed to determine the priority of radiography in trauma patients. This study aimed to investigate the role of machine learning models in predicting chest injuries following multiple trauma. Methods: We used the database of a comprehensive cross-sectional survey conducted in 2015. Eight machine learning models were developed using demographic characteristics, physical exam findings, and radiologic results of 2860 patients. Results: Area under the receiver operating characteristic curve (AUC) was greater than 0.96 in Random Forest, Gradient Boosting, XGBoost, Decision Tree, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), and Neural Network models. The random forest model, XGBoost and Gradient Boosting had the highest accuracy (0.99). Sensitivity was also highest in the Gradient Boosting, XGBoost and KNN models (0.99). The specificity of all of the models in predicting chest radiography outcomes of multiple trauma patients was higher than 0.97, except for logistic regression and SVM (0.912 and 0.885 respectively). Conclusion: Our study highlights the strong potential of machine learning models, especially Random Forest and Gradient Boosting, in predicting chest trauma outcomes with high accuracy and sensitivity. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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