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    科学领域:

    • 生物统计学 生物统计学
    • 机器学习 机器学习
    • 生存分析的分析.

    背景情况:

    • 使用治愈分数预测时间到事件数据具有挑战性.
    • 现有的方法可能难以处理复杂的关系和高维数据.

    研究的目的:

    • 为灵活的生存模型开发一种新的深度神经网络 (DNN) 框架.
    • 准确预测时间到事件数据在存在的治疗分数.
    • 为了捕捉非线性关系和高维交互.

    主要方法:

    • 将灵活的生存模型集成到深度神经网络 (DNN) 中.
    • 为了预测器的识别性,使用了一个正交层.
    • 线性,非线性和相互作用效应的添加分解.

    主要成果:

    • 与现有方法相比,证明了优越的预测性能.
    • 通过模拟实现了计算效率.
    • 已成功应用于美国大型抵押贷款数据集.

    结论:

    • 拟议的基于DNN的灵活生存模型提供了更高的预测准确性.
    • 该方法提供了对生存数据中共变量效应的更现实的理解.
    • 这种方法适用于生物统计学和金融领域的大规模应用.