深度集群生存机器具有可解释的专家分布
Bojian Hou1, Hongming Li1, Zhicheng Jiao2
1Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA.
Proceedings. IEEE International Symposium on Biomedical Imaging
|October 4, 2023
概括
我们引入了深度集群生存机器,以增强生存分析和数据异质性表征. 这种新的方法改善了时间到事件的预测,并揭示了超越传统方法的隐藏数据模式.
科学领域:
- 机器学习 机器学习
- 生存分析的分析.
- 数据挖掘 数据挖掘
背景情况:
- 传统的生存分析方法往往无法捕捉复杂的数据异质性.
- 需要先进的模型,可以同时预测存活率和描述底层数据结构.
研究的目的:
- 开发深度聚类生存机器,用于综合生存预测和异质性表征.
- 解决传统生存分析在建模复杂数据集中的局限性.
主要方法:
- 采用一种具有参数分布 (专家分布) 混合的生成方法来建模生存数据时间.
- 使用一种歧视性方法,在专家分布中学习实例特定的特征权重.
- 整合生成性和歧视性学习为全面的生存分析框架.
主要成果:
- 在真实和合成数据集上展示了有希望的集群结果.
- 在时间到事件预测方面取得了竞争性表现.
- 成功描述了数据异质性,通常不是通过常规方法建模的.
结论:
- 深度集群生存机器为生存分析提供了一种强大的新方法.
- 该方法有效地处理数据异质性,并提高预测准确性.
- 这一框架通过整合集群和生存预测来推进该领域.
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