发表机构
Indian Institute of Technology Guwahati（印度理工学院古瓦哈蒂分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
KESurv提出以生存森林为主模型、结合Beran核估计器的集成方法，在四个医疗数据集上优于基线，提升患者生存曲线预测的校准与排序性能。
AI 中文摘要
预测患者特异性生存函数对于临床医生在患者护理和治疗策略方面做出明智决策至关重要。在现有的各种模型中，生存森林已在众多场景中展现出显著的有效性。在本工作中，我们提出了一种集成方法，该方法以生存森林作为主模型，并辅以多个基础模型，充分发挥其优势。该集成方法引入了Beran估计器（一种核估计器），以增强患者特异性生存曲线的预测。我们使用四个不同的医疗数据集评估了所提模型的性能。结果表明，在大多数数据集上，我们的集成方法在校准和排序方面均优于基线模型。这些发现表明，我们的方法能够更准确、更可靠地估计患者特异性生存函数，为临床决策提供了有价值的工具。
英文摘要
Predicting patient-specific survival functions is crucial for clinicians in making informed decisions about patient care and treatment strategies. Among the various models available, the Survival Forest has demonstrated significant effectiveness in numerous scenarios. In this work, we propose an ensemble method that leverages the strengths of the Survival Forest as the master model, complemented by several base models. This ensemble incorporates the Beran estimator, a type of kernel estimator, to enhance predictions of patient-specific survival curves. We evaluated the performance of our proposed model using four distinct healthcare datasets. The results highlight the superiority of our ensemble method over baseline models in both calibration and ranking across most datasets. The findings suggest that our approach offers a more accurate and reliable estimation of patient-specific survival functions, providing a valuable tool for clinical decision-making.
