Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional survivorship estimators (such as Kaplan-Meier) or competing risk estimators. Recent advances in machine learning survival analysis might improve decision support tools in this setting. Therefore, this study aimed to assess the performance of machine learning compared with that of conventional modeling to predict revision after arthroplasty.

中文翻译：

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机器学习在预测修复关节置换术方面的表现并不优于传统的竞争风险模型。

估计关节置换术后翻修的风险可以为患者和外科医生的决策提供信息。然而，缺乏性能良好的预测模型来协助这项任务，这可能是由于当前的传统建模方法，例如传统的生存估计器（例如 Kaplan-Meier）或竞争风险估计器。机器学习生存分析的最新进展可能会改善这种情况下的决策支持工具。因此，本研究旨在评估机器学习与传统模型相比的性能，以预测关节置换术后的修复情况。