Updating Stochastic Networks to Integrate Cross-Sectional and Longitudinal Studies
Updating Stochastic Networks to Integrate Cross-Sectional and Longitudinal Studies
复制标题
更新随机网络以整合横截面和纵向研究
DOI：
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复制
发表时间：
2015
期刊：
影响因子：
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通讯作者：
Yuanxi Li
中科院分区：
文献类型：
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作者：
A. Tucker;Yuanxi Li
临床试验通常在规定的时间段内对人群进行，以阐明健康问题或疾病过程的某些特征。这些横断面研究提供了大量人群的这些疾病过程的快照，但不允许我们对疾病的时间性质进行建模，这对于建模详细的预后预测至关重要。另一方面，纵向研究用于探索这些过程如何随着时间的推移在许多人中发展，但可能昂贵且耗时，许多研究仅涵盖疾病过程中相对较小的窗口。本文探讨了应用智能数据分析技术从横向和纵向研究中建立可靠的疾病进展模型。其目的是通过建立从健康患者到晚期疾病患者的真实轨迹，从横截面数据中学习疾病“轨迹”。我们专注于探索我们是否可以“校准”的模型，从这些轨迹与真实的纵向数据使用鲍姆-韦尔奇重新估计。
Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This paper explores the application of intelligent data analysis techniques for building reliable models of disease progression from both cross-sectional and longitudinal studies. The aim is to learn disease ‘trajectories’ from cross-sectional data by building realistic trajectories from healthy patients to those with advanced disease. We focus on exploring whether we can ‘calibrate’ models learnt from these trajectories with real longitudinal data using Baum-Welch re-estimation.