A richly interactive exploratory data analysis and visualization tool using electronic medical records.
A richly interactive exploratory data analysis and visualization tool using electronic medical records.
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DOI：
10.1186/s12911-015-0218-7
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发表时间：
2015-11-12
影响因子：
3.5
通讯作者：
Jian WS
中科院分区：
文献类型：
--
作者：
Huang CW;Lu R;Iqbal U;Lin SH;Nguyen PAA;Yang HC;Wang CF;Li J;Ma KL;Li YJ;Jian WS
电子病历(EMR)包含医生、临床研究人员和医疗决策者非常感兴趣的海量数据。随着EMR的规模、复杂性和可访问性的增长，从其中提取有意义的信息的能力已经成为一个日益重要的问题需要解决。我们开发了标准化的数据分析流程，以支持专注于特定疾病的队列研究。我们使用一种互动的分而治之的方法将患者分类为每组中相对一致的患者。这是一个重复的过程，使用户能够将数据划分为同质子集，这些子集可以通过视觉进行检查、比较和改进。最终的可视化是由转换后的数据驱动的，用户反馈给相应的操作员，完成重复的过程。输出结果显示在Sankey图表样式的时间线中，这是一种特殊的流程图，用于显示因素随时间的状态和转变。这篇论文展示了一个视觉丰富、基于Web的交互式应用程序，它可以使研究人员通过使用电子病历数据来研究随时间推移的任何队列。由此产生的可视化有助于发现数据中隐藏的信息，比较患者组之间的差异，确定影响特定疾病的关键因素，并帮助指导进一步的分析。我们通过使用14,567名慢性肾脏疾病(CKD)患者的EMR介绍并演示了这一工具。我们开发了一个可视化挖掘系统来支持对多维分类电子病历数据的探索性数据分析。以慢性肾脏病作为疾病模型，通过自动相关分析和人工视觉评估对其进行组装。可视化方法，如桑基图，可以揭示关于特定疾病队列的有用知识以及疾病随着时间的推移的轨迹。
Electronic medical records (EMRs) contain vast amounts of data that is of great interest to physicians, clinical researchers, and medial policy makers. As the size, complexity, and accessibility of EMRs grow, the ability to extract meaningful information from them has become an increasingly important problem to solve. We develop a standardized data analysis process to support cohort study with a focus on a particular disease. We use an interactive divide-and-conquer approach to classify patients into relatively uniform within each group. It is a repetitive process enabling the user to divide the data into homogeneous subsets that can be visually examined, compared, and refined. The final visualization was driven by the transformed data, and user feedback direct to the corresponding operators which completed the repetitive process. The output results are shown in a Sankey diagram-style timeline, which is a particular kind of flow diagram for showing factors’ states and transitions over time. This paper presented a visually rich, interactive web-based application, which could enable researchers to study any cohorts over time by using EMR data. The resulting visualizations help uncover hidden information in the data, compare differences between patient groups, determine critical factors that influence a particular disease, and help direct further analyses. We introduced and demonstrated this tool by using EMRs of 14,567 Chronic Kidney Disease (CKD) patients. We developed a visual mining system to support exploratory data analysis of multi-dimensional categorical EMR data. By using CKD as a model of disease, it was assembled by automated correlational analysis and human-curated visual evaluation. The visualization methods such as Sankey diagram can reveal useful knowledge about the particular disease cohort and the trajectories of the disease over time.