Angelelli Paolo, Oeltze Steffen, Haász Judit, Turkay Cagatay, Hodneland Erlend, Lundervold Arvid, Lundervold Astri J, Preim Bernhard, Hauser Helwig
IEEE Comput Graph Appl. 2014 Sep-Oct;34(5):70-82. doi: 10.1109/MCG.2014.40.
Medical cohort studies enable the study of medical hypotheses with many samples. Often, these studies acquire a large amount of heterogeneous data from many subjects. Usually, researchers study a specific data subset to confirm or reject specific hypotheses. A new approach enables the interactive visual exploration and analysis of such data, helping to generate and validate hypotheses. A data-cube-based model handles partially overlapping data subsets during the interactive visualization. This model enables seamless integration of the heterogeneous data and the linking of spatial and nonspatial views of the data. Researchers implemented this model in a prototype application and used it to analyze data acquired in a cohort study on cognitive aging. Case studies employed the prototype to study aspects of brain connectivity, demonstrating the model's potential and flexibility.
医学队列研究能够利用大量样本对医学假设进行研究。通常，这些研究从众多受试者那里获取大量异质性数据。一般来说，研究人员会研究特定的数据子集，以证实或否定特定假设。一种新方法能够对此类数据进行交互式可视化探索与分析，有助于生成并验证假设。基于数据立方体的模型在交互式可视化过程中处理部分重叠的数据子集。该模型能够无缝集成异质性数据，并将数据的空间视图与非空间视图相链接。研究人员在一个原型应用程序中实现了该模型，并利用它来分析在一项关于认知衰老的队列研究中获取的数据。案例研究使用该原型来研究大脑连通性的各个方面，证明了该模型的潜力和灵活性。
