变压器LSR: 纵向数据,存活率和具有并发潜结构的重复事件的注意力联合模型
Zhiyue Zhang1, Yao Zhao1, Yanxun Xu2
1Department of Applied Mathematics and Statistics, Johns Hopkins University, 3100 Wyman Park Dr, Baltimore, 21211, MD, USA.
Artificial intelligence in medicine
|December 20, 2024
概括
我们介绍了TransformerLSR,这是一个深度学习框架,用于联合建模纵向数据,反复事件和生存数据. 这种方法解决了现有方法的局限性,为复杂的生物医学和社会科学研究提供了灵活的解决方案.
科学领域:
- 生物统计学 生物统计学
- 机器学习 机器学习
- 流行病学 流行病学
背景情况:
- 纵向,反复和生存数据的联合建模在生物医学和社会科学中至关重要.
- 现有的统计模型通常依赖于强大的参数假设,并面临可扩展性挑战.
- 当前的深度学习方法通常忽略反复发生的事件或假定定期的纵向数据间隔.
研究的目的:
- 开发一个灵活的深度学习框架,TransformerLSR,用于同时联合建模纵向测量,反复事件和生存数据.
- 解决现有方法在参数假设,可扩展性和处理反复事件方面的局限性.
- 结合深度时间点过程和用于增强建模的新型轨迹表示.
主要方法:
- 开发了基于变压器的深度建模和推理框架TransformerLSR.
- 集成的深度时间点流程,以模拟反复和终端事件作为竞争过程.
- 引入了对纵向变量的新型轨迹表示和模型架构.
- 利用模拟研究和现实世界移植数据集进行验证.
主要成果:
- 变压器LSR有效地模拟了纵向,反复和生存数据之间的复杂依赖关系.
- 该框架展示了灵活性,并解决了以前联合建模方法的局限性.
- 对脏移植数据的分析强调了变压器LSR的实际实用性.
结论:
- 变压器LSR提供了一种强大而灵活的深度学习解决方案,用于纵向,反复和生存数据的联合建模.
- 提出的方法在处理复杂的依赖关系方面是有效的,并有可能结合先前的知识.
- 该框架促进了各种科学领域多变量过程的联合建模.
相关概念视频
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01:18Introduction To Survival Analysis
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