Combining Visual Cleansing and Exploration for Clinical Data

Combining Visual Cleansing and Exploration for Clinical Data
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结合视觉清理和临床数据探索

DOI:
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发表时间:
2019
期刊:
Workshop on Visual Analytics in Healthcare
影响因子:
--
通讯作者:
H. Schumann
H. Schumann
中科院分区:
--
文献类型:
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作者:
Christoph Schmidt;M. Röhlig;Bastian Grundel;P. Daumke;M. Ritter;A. Stahl;Paul Rosenthal;H. Schumann

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临床数据有其自身的特点,因为它们随着时间的推移而发展,可能是不完整的,并且是高度异质的。这些特征使彻底的分析成为一项具有挑战性的任务,特别是因为领域专家知道数据缺陷,这可能会影响他们对数据的信任。当我们从3,500多名视网膜疾病患者中获得匿名临床数据时,我们必须应对这些挑战。我们定义了一个工作流,在迭代过程中集成了数据清理和探索,以便用户能够在分析的任何时候轻松地发现数据中的异常和模式。我们在以用户为中心的可视化分析工具中实施我们的工作流程,并采用专用的可视化和交互技术。我们与专家合作,应用我们的工具来检查患者视力发展和治疗模式之间的相互依赖性。我们发现,现实生活中的数据往往有不可预见的事件,可以强烈影响整体视力的发展。这与研究结果不同,研究结果通常是在限制性条件下进行的,并且显示了按计划治疗的视力改善。
Clinical data have their own peculiarities, as they evolve over time, may be incomplete, and are highly heterogeneous. These characteristics turn a thorough analysis into a challenging task, especially since domain experts are aware of the data flaws, which may impact their trust in the data. As we obtained anonymized clinical data from more than 3,500 patients with retinal diseases, we have to address these challenges. We define a workflow that integrates data cleansing and exploration in an iterative process, so that users are able to easily find anomalies and patterns in the data at any point in their analysis. We implement our workflow in a user-centered visual analytics tool with dedicated visualization and interaction techniques. In collaboration with experts, we apply our tool to examine the interdependency between patients’ visual acuity developments and treatment patterns. We find, that real-life data often have unforeseen incidents which can strongly influence the overall visual acuity development. This differs to study results, which are usually conducted under restrictive conditions and have shown visual acuity improvement with on-schedule treatment.
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发表时间: 2019-01
影响因子: 5.2
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