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sEBM:扩展基于事件的模型,通过隐式生物标志物选择和集群来预测疾病进展

Raghav Tandon^1,2, Anna Kirkpatrick^1,3, Cassie S Mitchell^1,2

¹Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA.

Information processing in medical imaging : proceedings of the ... conference

|April 29, 2024

概括

缩放的基于事件的模型 (sEBM) 通过选择相关的生物标志物并将其集群起来,使许多生物标志物能够进行疾病进展建模. 这种方法成功地分层了阿尔茨海默病患者,并预测了转化风险.

科学领域:

计算生物学和生物信息学
生物统计学和数据科学
神经科学和神经退行性疾病

背景情况:

基于事件的模型 (EBM) 是一种通过生物标志物事件进行疾病进展的概率生成模型.
由于可能的事件序列的组合式爆炸,传统的EBM在高维数据方面扎.
马尔科夫链蒙特卡洛 (MCMC) 用于后部分布采样,但在许多生物标志物上变得难以计算.

研究的目的:

开发一种可扩展的基于事件的模型 (sEBM),用于分析疾病进展中的高维生物标记数据.
隐式选择相关的生物标志物并将它们聚集在一起,以减少事件序列推断的复杂性.
在合成和现实世界的临床数据上验证sEBM的性能,包括阿尔茨海默病的进展.

主要方法:

缩放的基于事件的模型 (sEBM) 隐式地选择了生物标志物的子集,用于事件序列推断.
sEBM集群具有类似事件序列位置的生物标志物,排序集群而不是单个生物标志物.
该方法使用合成数据进行了验证,并应用于阿尔茨海默病神经成像计划 (ADNI) 数据.

主要成果:

sEBM显著减少了可能发生的事件序列的空间,使得最多300个生物标志物的分析成为可能.
在合成数据上,sEBM在更高的维度设置中优于以前的EBM变体.
应用到ADNI数据成功地将受试者分层 (认知正常,MCI,AD) 分为6个疾病阶段.
增加的sEBM阶段强烈预测了MCI患者转化为阿尔茨海默病的风险.

结论:

缩放式基于事件的模型 (sEBM) 有效地解决了传统EBM在高维生物标记数据上的可扩展性限制.
sEBM为建模疾病进展和分层患者提供了一个强大的框架,而不依赖先验诊断标签.
该方法显示出临床应用的巨大潜力,特别是预测神经退行性疾病的疾病进展和转化风险.

关键词:

贝叶斯式学习是贝叶斯式学习.生物标志物聚类生物标志物聚类.疾病进展建模模型.预后生物标志物选择预后生物标志物选择

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