动态事件时间数据的多任务Logistic生存预测方法

Multi-task Logistic survival prediction method for time-dependent time-to-event data

事件时间数据广泛存在于临床医学研究领域,包含大量复杂的随时间变化的动态风险因子变量。为了对这些动态事件时间数据进行有效分析,克服生存模型参数假设的局限性,提出了一种多任务Logistic生存学习和预测方法。将生存预测转化为一系列不同时间点的多任务二元生存分类问题,利用动态风险因子变量的全部观测值估计累积风险。通过对事件样本和删失样本的全数据学习正则化Logistic回归参数。评估风险因子与事件时间的动态关系,根据生存概率估计事件时间。在多个实际临床数据集上开展的对比实验验证了提出的多任务预测方法对于动态数据不仅具有较强的适用性,而且能够保障预测结果的准确性和可靠性。

Time-to-event data are ubiquitous in clinical medicine research domain,and include a large number of timedependent time-dependent risk factor variables.To effectively analyze the time-dependent time-to-event data and to overcome the limitation of parameter hypothesis of the survival model,a multi-task Logistic survival leaning and prediction method was proposed.The survival prediction was transformed into a series of multi-task binary survival classification problems at various time points,and all observations of time-dependent risk factors were used to estimate the cumulative risk.By learning all data of event samples and censored samples,the Logistic regression parameters were regularized.The time-dependent relationships between risk factors and time-to-event were evaluated,and the time-to-event was estimated according to the survival probability.The comparative experiments on multiple real clinical datasets demonstrate the applicability of the proposed multi-task prediction method for time-dependent data and that the method can guarantee the accuracy and reliability of the prediction results.