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INTERACTIVE VISUAL ANALYSIS OF INTENSIVE CARE UNIT DATA: Relationship between Serum Sodium Concentration, its Rate of Change and Survival Outcome

机译:重症监护单元数据的交互式视觉分析:血清钠浓度之间的关系,其变化率和生存结果

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In this paper we present a case study of interactive visual analysis and exploration of a large ICU data set. The data consists of patients' records containing scalar data representing various patients' parameters (e.g. gender, age, weight), and time series data describing logged parameters over time (e.g. heart rate, blood pressure). Due to the size and complexity of the data, coupled with limited time and resources, such ICU data is often not utilized to its full potential, although its analysis could contribute to a better understanding of physiological, pathological and therapeutic processes, and consequently lead to an improvement of medical care. During the exploration of this data we identified several analysis tasks and adapted and improved a coordinated multiple views system accordingly. Besides a curve view which also supports time series with gaps, we introduced a summary view which allows an easy comparison of subsets of the data and a box plot view in a coordinated multiple views setup. Furthermore, we introduced an inverse brush, a secondary brush which automatically selects non-brushed items, and updates itself accordingly when the original brush is modified. The case study describes how we used the system to analyze data from 1447 patients from the ICU at Guy's & St. Thomas' NHS Foundation Trust in London. We were interested in the relationship between serum sodium concentration, its rate of change and their effect on ICU mortality rates. The interactive visual analysis led us to findings which were fascinating for medical experts, and which would be very difficult to discover using conventional analysis methods usually applied in the medical field. The overall feedback from domain experts (coauthors of the paper) is very positive.
机译:在本文中,我们提出交互式可视化分析和大数据ICU集的探索为例。该数据包括患者参数(例如性别,年龄,体重)包含表示各个患者的标量数据记录,并且时间序列数据描述随时间记录参数(例如心脏速率,血压)。由于尺寸和数据的复杂性,加上有限的时间和资源,例如ICU数据通常不用于其全部潜力,尽管它的分析可能有助于更好地理解生理,病理和治疗过程的,并因此导致医疗保健的改善。在这个数据的探索,我们确定了几个分析任务和适应并相应提升协调多个视图系统。此外还支持有间隙的时间序列曲线图,我们引入了一个摘要视图允许数据,并以协调多个视图设置一个箱形图视图的子集的一个简单的比较。此外,我们引入了一个逆刷,其中自动选择非刷项的辅助刷,并且当原始刷被修改相应地更新自身。案例研究描述了我们如何使用该系统来分析盖伊与圣托马斯NHS信托基金会在伦敦从ICU病人1447的数据。我们感兴趣的血清钠浓度,其变化率及其对ICU死亡率的影响之间的关系。交互式可视化分析,使我们这都是很吸引人的医学专家发现,并且将很难使用通常在医疗领域的应用常规的分析方法来发现。从领域专家的总体反馈(论文合着者的)是非常积极的。

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