tdCoxSNN:时间依赖的Cox生存神经网络,用于连续时间动态预测
Lang Zeng1, Jipeng Zhang1, Wei Chen1,2
1Department of Biostatistics and Health Data Science, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
概括
我们开发了一种新的依赖时间的Cox生存神经网络 (tdCoxSNN),用于使用纵向 fundus 图像进行与年龄相关的黄斑变性 (AMD) 进展的动态风险预测. 这种方法随着时间的推移提供了改进的个性化预测.
科学领域:
- 医疗成像医学成像
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 动态预测提供不断变化的,个性化的风险评估.
- 与年龄相关的黄斑变性 (AMD) 是一种渐进的眼睛疾病,需要准确的预后工具.
- 现有的预测模型可能无法充分利用纵向数据,特别是成像.
研究的目的:
- 提出一种新的深度学习模型,即时间依赖的考克斯生存神经网络 (tdCoxSNN),用于动态预测AMD进展.
- 将纵向 fundus 图像直接集成到一个生存预测框架中.
- 为了评估tdCoxSNN的性能与既定方法对比.
主要方法:
- 开发了一个依赖时间的考克斯生存神经网络 (tdCoxSNN),将考克斯生存模型与深度学习相结合.
- 在tdCoxSNN中使用卷积神经网络 (CNN) 来处理纵向 fundus 图像.
- 整合了时间依赖的共变量,以捕捉不断变化的风险因素.
- 通过模拟将tdCoxSNN与联合建模和标志方法进行比较.
主要成果:
- 在广泛的模拟研究中,tdCoxSNN表现出了值得称赞的预测性能.
- 当该模型应用于与年龄相关的眼病研究 (AREDS) 数据集时,它取得了强有力的结果.
- 该方法还在一次性胆汁硬化数据集上进行了验证,用于预测肝移植时间.
结论:
- 拟议的tdCoxSNN有效地使用纵向成像数据预测疾病进展.
- 这种深度学习方法增强了对AMD等进展性疾病的动态风险预测.
- tdCoxSNN在临床实践中提供了一个强大的工具,用于个性化,随时间变化的风险评估.
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