Integrated Visual Analysis for Heterogeneous Datasets in Cohort Studies

Integrated Visual Analysis for Heterogeneous Datasets in Cohort Studies
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队列研究中异构数据集的集成可视化分析

DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
C. Botha
C. Botha
中科院分区：
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文献类型：
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作者：
M. Steenwijk;J. Milles;M. Buchem;J. Reiber;C. Botha

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目前的医学研究往往是假设驱动的，集中在有限数量的参数显示，或期望显示，与疾病的一些关系。然而，当缺乏支持的科学依据或适当的假设时，这种方法并不总是富有成效的。视觉分析在医学研究中的应用有限。我们建议视觉分析可以用于研究患者的参数，特别是在从一开始就没有明确假设的情况下。这可以帮助医学研究人员集中精力。我们提出了一个可视化分析框架，它提供了高度交互式的队列数据可视化分析，并能够处理不规则的多时间点，成像和非成像数据。该框架将特征提取集成到可视化分析过程中，并利用精心设计的数据结构，能够跟踪非同构队列研究数据中的数据依赖关系和相互关系。我们在一组疑似患有神经精神性SLE（一种异质性风湿病）的患者中评估了该框架。目视分析揭示了一些证实早期发现的观察结果。我们还能够识别数据中的新趋势，这些趋势可以为进一步的研究指明方向，并由此说明可视化分析作为假设生成工具的潜力。
Current medical research is often hypothesis driven, focusing on a limited number of parameters showing, or expected to show, some relation with the disease. When a supporting scientific ground or proper hypothesis is lacking however, this approach is not always fruitful. Visual analytics has seen limited application in medical research. We propose that visual analytics can be used to study parameters across patients, especially in cases where no clear hypothesis is available from the start. This can help medical researchers to focus their efforts. We present a visual analysis framework which provides highly interactive visual analysis of cohort data, and is able to deal with irregular multi-timepoint, imaging and non-imaging data. The framework integrates the extraction of features into the process of visual analysis and makes use of a carefully designed data structure able to keep track of data dependencies and interrelationships in inhomogeneous cohort study data. We evaluated the framework on a cohort of patients suspected of having neuropsychiatric SLE, a heterogeneous rheumatic disease. Visual analysis revealed a number of observations corroborating earlier findings. We were also able to identify new trends in the data that could indicate directions for further research, and illustrated thereby the potential of visual analytics to operate as a hypothesis generating tool.