Shahverdi Kondori Mohsen, Malek Hamed
Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran.
Arch Acad Emerg Med. 2021 Jan 24;9(1):e15. doi: 10.22037/aaem.v9i1.1060. eCollection 2021.
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.
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.
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.
Considering its high sensitivity, the proposed model seems to be sufficiently reliable for determining the need for performing a CT scan.
计算机断层扫描（CT）在急诊医学中对于创伤患者的诊断至关重要。随着机器学习的发展，已经开展了大量关于指导医学检查的研究，从而实现更准确、快速的诊断。本研究旨在提出一种基于机器学习的方法，以帮助急诊医生避免对胸部创伤患者进行不必要的CT扫描。
使用了近两年来收集的1000个样本的数据集。用于建模的分类方法包括支持向量机（SVM）、逻辑回归、朴素贝叶斯、决策树、多层感知器（四个隐藏层）、随机森林和K近邻（KNN）。本研究采用决策树方法（最具可解释性的机器学习方法）作为最终方法。
研究了7种机器学习算法的准确性。决策树算法的准确性高于其他算法。使用训练数据选择了最优树深度为7。最终模型的准确性、敏感性和特异性分别计算为99.91%（95%CI：99.10% - 100%）、100%（95%CI：99.89% - 100%）和99.33%（95%CI：99.10% - 99.56%）。
考虑到其高敏感性，所提出的模型对于确定是否需要进行CT扫描似乎足够可靠。
