PDF(Pubmed)
Abstract：
UNASSIGNED: Prolonged wound drainage (PWD) is one of the most important reasons that increase the risk of early periprosthetic joint infection after arthroplasty. It is very important to evaluate the risk factors for PWD in the surgical field after arthroplasty surgery. This can be accomplished using machine learning or artificial intelligence methods. Our aim in this study was to compare machine learning methods in predicting possible PWD.
UNASSIGNED: The study was carried out on clinical, laboratory, and radiological data of 313 patients who underwent hemiarthroplasty (HA) for proximal femur fractures. We preprocessed the dataset and trained and tested machine learning methods using cross validation. We compared various machine learning algorithms (linear discriminant analysis, decision tree, k-nearest neighbors, gradient boosting machine, and logistic regression [LR]) based on performance measures. We also combined the most successful algorithms with a metaclassifier. To help understand the relationship between risk factors, we provided a risk factor severity ranking.
UNASSIGNED: To estimate the risk of PWD, classification was performed with first-level classifiers and then integrated as a LR-based meta-learner stacking method. More performance improvements were achieved with the stacking method.
UNASSIGNED: We found that the stacking method was superior to other methods in PWD classification. We determined that the volume of fluid collected from the drain, morbid obesity class, blood transfusion, and body mass index score were the four most important risk factors according to stacking.
UNASSIGNED: The study was carried out on clinical, laboratory, and radiological data of 313 patients who underwent hemiarthroplasty (HA) for proximal femur fractures. We preprocessed the dataset and trained and tested machine learning methods using cross validation. We compared various machine learning algorithms (linear discriminant analysis, decision tree, k-nearest neighbors, gradient boosting machine, and logistic regression [LR]) based on performance measures. We also combined the most successful algorithms with a metaclassifier. To help understand the relationship between risk factors, we provided a risk factor severity ranking.
UNASSIGNED: To estimate the risk of PWD, classification was performed with first-level classifiers and then integrated as a LR-based meta-learner stacking method. More performance improvements were achieved with the stacking method.
UNASSIGNED: We found that the stacking method was superior to other methods in PWD classification. We determined that the volume of fluid collected from the drain, morbid obesity class, blood transfusion, and body mass index score were the four most important risk factors according to stacking.
摘要：
■长时间的伤口引流（PWD）是增加关节成形术后早期假体周围感染风险的最重要原因之一。在关节置换术后手术领域评估PWD的危险因素非常重要。这可以使用机器学习或人工智能方法来实现。我们在这项研究中的目的是比较机器学习方法在预测可能的PWD。
■这项研究是在临床上进行的，实验室,313例股骨近端骨折患者的影像学资料。我们对数据集进行了预处理，并使用交叉验证对机器学习方法进行了训练和测试。我们比较了各种机器学习算法(线性判别分析，决策树,k-最近的邻居，梯度增压机，和逻辑回归[LR])基于绩效指标。我们还将最成功的算法与元分类器结合在一起。为了帮助理解风险因素之间的关系，我们提供了风险因素严重程度排序.
■为了估计PWD的风险，使用一级分类器进行分类,然后整合为基于LR的元学习器堆叠方法.使用堆叠方法实现了更多的性能改进。
■我们发现堆叠方法在PWD分类中优于其他方法。我们确定从排水管收集的液体量，病态肥胖类，输血,和体重指数评分是四个最重要的危险因素。
■这项研究是在临床上进行的，实验室,313例股骨近端骨折患者的影像学资料。我们对数据集进行了预处理，并使用交叉验证对机器学习方法进行了训练和测试。我们比较了各种机器学习算法(线性判别分析，决策树,k-最近的邻居，梯度增压机，和逻辑回归[LR])基于绩效指标。我们还将最成功的算法与元分类器结合在一起。为了帮助理解风险因素之间的关系，我们提供了风险因素严重程度排序.
■为了估计PWD的风险，使用一级分类器进行分类,然后整合为基于LR的元学习器堆叠方法.使用堆叠方法实现了更多的性能改进。
■我们发现堆叠方法在PWD分类中优于其他方法。我们确定从排水管收集的液体量，病态肥胖类，输血,和体重指数评分是四个最重要的危险因素。
