这项研究旨在开发和验证五种机器学习模型,这些模型旨在预测颌骨放线菌骨髓炎。此外,这项研究确定了颌骨放线菌骨髓炎的预测变量的相对重要性,这对临床决策至关重要。
共分析了222例颌骨骨髓炎患者,放线菌70例(31.5%)。Logistic回归,随机森林,支持向量机,人工神经网络,和极端梯度增强机器学习方法用于训练模型。随后使用测试数据集对模型进行了验证。这些模型相互比较,也与单一预测因子进行比较,比如年龄,使用接收器工作特征下面积(ROC)曲线(AUC)。
机器学习模型的AUC范围从0.81到0.88。机器学习模型的性能,比如随机森林,支持向量机和极端梯度提升显著优于单预测因子。推测原因,抗吸收剂,年龄,恶性肿瘤,高血压,和类风湿性关节炎是被确定为相关预测因子的六个特征。
该预测模型将通过增强预后咨询和为颌骨放线菌骨髓炎高危人群提供治疗决策,从而改善整体患者护理。
This
study aimed to develop and validate five machine learning models designed to predict actinomycotic osteomyelitis of the jaw. Furthermore, this
study determined the relative importance of the predictive variables for actinomycotic osteomyelitis of the jaw, which are crucial for clinical decision-making.
A total of 222 patients with osteomyelitis of the jaw were analyzed, and Actinomyces were identified in 70 cases (31.5%). Logistic regression, random forest, support vector machine, artificial neural network, and extreme gradient boosting machine learning methods were used to train the models. The models were subsequently validated using testing datasets. These models were compared with each other and also with single predictors, such as age, using area under the receiver operating characteristic (ROC) curve (AUC).
The AUC of the machine learning models ranged from 0.81 to 0.88. The performance of the machine learning models, such as random forest, support vector machine and extreme gradient boosting was significantly superior to that of single predictors. Presumed causes, antiresorptive agents, age, malignancy, hypertension, and rheumatoid arthritis were the six features that were identified as relevant predictors.
This prediction model would improve the overall patient care by enhancing prognosis counseling and informing treatment decisions for high-risk groups of actinomycotic osteomyelitis of the jaw.