This paper introduces a Random Survival Forest (RSF) method for functional data. The focus is specifically on defining a new functional data structure, the Censored Functional Data (CFD), for dealing with temporal observations that are censored due to study limitations or incomplete data collection. This approach allows for precise modelling of functional survival trajectories, leading to improved interpretation and prediction of survival dynamics across different groups. A medical survival study on the benchmark SOFA data set is presented. Results show good performance of the proposed approach, particularly in ranking the importance of predicting variables, as captured through dynamic changes in SOFA scores and patient mortality rates.

翻译：本文提出了一种适用于函数型数据的随机生存森林方法。研究重点在于定义一种新的函数型数据结构——删失函数型数据，用于处理因研究限制或数据收集不完整而出现删失的时序观测。该方法能够精确建模函数型生存轨迹，从而提升对不同群体生存动态的解释与预测能力。研究基于基准SOFA数据集进行了医学生存分析，结果表明所提方法具有良好性能，尤其在通过SOFA评分动态变化和患者死亡率揭示预测变量重要性排序方面表现突出。