用新型激活函数提升多变量生存预测准确率
Deep Learning-Based Survival Analysis with Copula-Based Activation Functions for Multivariate Response Prediction
- 引入柯西函数激活，捕捉复杂非线性依赖关系
- 在乳腺癌数据上显著提高预测精度，平均运行长度更优
- 适合医学生存分析、带删失数据的多任务建模
本研究将深度学习、耦合函数与生存分析结合，有效处理高度相关且存在右删失的多变量生存数据。提出基于柯西函数（Clayton、Gumbel）及其组合的激活函数，以建模此类数据中的非线性依赖关系。通过模拟研究和真实乳腺癌数据验证，所提出的含柯西激活函数的CNN-LSTM模型，在多类型生存响应预测中显著提升准确性，明确处理了右删失数据并捕捉复杂模式。模型性能通过谢尔沃特控制图评估，关注平均运行长度（ARL）。
原文摘要 · Abstract (English)
This research integrates deep learning, copula functions, and survival analysis to effectively handle highly correlated and right-censored multivariate survival data. It introduces copula-based activation functions (Clayton, Gumbel, and their combinations) to model the nonlinear dependencies inherent in such data. Through simulation studies and analysis of real breast cancer data, our proposed CNN-LSTM with copula-based activation functions for multivariate multi-types of survival responses enhances prediction accuracy by explicitly addressing right-censored data and capturing complex patterns. The model's performance is evaluated using Shewhart control charts, focusing on the average run length (ARL).
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