Determining the Need for Computed Tomography Scan Following Blunt Chest Trauma through Machine Learning Approaches
Archives of Academic Emergency Medicine,
Vol. 9 No. 1 (2021),
1 January 2021
,
Page e15
https://doi.org/10.22037/aaem.v9i1.1060
Abstract
Introduction: The use of computed tomography (CT) scan is essential for making diagnoses for trauma patients in emergency medicine. Numerous studies have been conducted on guiding medical examinations in light of advances in machine learning, leading to more accurate and rapid diagnoses. The present study aims to propose a machine learning-based method to help emergency physicians prevent performance of unnecessary CT scans for chest trauma patients.
Methods: A dataset of 1000 samples collected in nearly two years was used. Classification methods used for modeling included the support vector machine (SVM), logistic regression, Naïve Bayes, decision tree, multilayer perceptron (four hidden layers), random forest, and K nearest neighbor (KNN). The present work employs the decision tree approach (the most interpretable machine learning approach) as the final method.
Results: The accuracy of 7 machine learning algorithms was investigated. The decision tree algorithm was of higher accuracy than other algorithms. The optimal tree depth of 7 was chosen using the training data. The accuracy, sensitivity and specificity of the final model was calculated to be 99.91% (95%CI: 99.10% – 100%), 100% (95%CI: 99.89% – 100%), and 99.33% (95%CI: 99.10% – 99.56%), respectively.
Conclusion: Considering its high sensitivity, the proposed model seems to be sufficiently reliable for determining the need for performing a CT scan.
- Radiography
- Tomography
- X-Ray Computed
- Clinical Decision Rules
- Decision Trees
- Machine Learning
How to Cite
References
Sangster GP, González-Beicos A, Carbo AI, Heldmann MG, Ibrahim H, Carrascosa P, et al. Blunt traumatic injuries of the lung parenchyma, pleura, thoracic wall, and intrathoracic airways: multidetector computer tomography imaging findings. Emergency Radiology. 2007;14(5):297-310.
Traub M, Stevenson M, McEvoy S, Briggs G, Lo SK, Leibman S, et al. The use of chest computed tomography versus chest X-ray in patients with major blunt trauma. Injury. 2007;38(1):43-7.
Shafaf N, Malek H. Applications of Machine Learning Approaches in Emergency Medicine; a Review Article. Archives of Academic Emergency Medicine. 2019;7(1).
Safari S, Farbod M, Hatamabadi H, Yousefifard M, Mokhtari N. Clinical predictors of abnormal chest CT scan findings following blunt chest trauma: A cross-sectional study. Chinese Journal of Traumatology. 2020;23(1):51-5.
Digital Design and Computer Architecture - 2nd Edition.
Khanna D, Sharma A, editors. Kernel-Based Naive Bayes Classifier for Medical Predictions2018 2018. Singapore: Springer.
Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. 2nd edition ed. New York, NY: Springer; 2016 2016/01/01/. 767 p.
Jiang L, Cai Z, Wang D, Jiang S, editors. Survey of Improving K-Nearest-Neighbor for Classification. Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007); 2007 2007/08//.
Molnar C. Interpretable Machine Learning: Lulu.com; 2020 2020/02/28/. 320 p.
Maleki S, Tran-Thanh L, Hines G, Rahwan T, Rogers A. Bounding the Estimation Error of Sampling-based Shapley Value Approximation. arXiv:13064265 [cs]. 2014.
- Abstract Viewed: 976 times
- pdf Downloaded: 476 times
