Trajectories Through the Disease Process: Cross Sectional and Longitudinal Studies
Trajectories Through the Disease Process: Cross Sectional and Longitudinal Studies
复制标题
疾病过程的轨迹：横断面和纵向研究
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Swift
中科院分区：
文献类型：
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作者：
A. Tucker;Yuanxi Li;S. Ceccon;S. Swift
本文探讨了使用两种不同的技术，从临床数据中建立疾病进展模型。首先，它探索了使用非平稳动态贝叶斯网络来建模疾病进展，其中基础模型随时间变化（这在许多疾病中很常见，其中一些组织或器官在疾病进展期间受到损伤）。其次，通过截面数据拟合轨迹，以便从较大的队列中建立进展模型，但没有任何印记。该方法适用于模拟数据和真实的临床数据的基础上，从青光眼患者的视野测试，在世界上的第二大致盲原因。结果表明，整合横截面和纵向数据的重要性，这两种数据都提供了不同的优势，以了解疾病的进展，并使用模型，占改变基础结构。
This paper explores the use of two different techniques for building models of disease progression from clinical data. Firstly, it explores the use of non-stationary dynamic Bayesian networks to model disease progression where the underlying model changes over time (as is common with many diseases where some tissue or organ becomes damaged throughout the duration of disease progression. Secondly, the fitting of trajectories through cross-sectional data in order to build models of progression from larger cohorts but without any stamps. The methods are applied to simulated data and real clinical data based on visual field tests from sufferers of glaucoma, the second largest cause of blindness in the world. Results demonstrate the importance of integrating cross-sectional and longitudinal data, both of which offer different advantages to understanding disease progression, and the use of models that account for changing underlying structures.
影响因子：
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作者：
Heijl, A;Leske, MC;Hussein, M
通讯作者：
Hussein, M