Huang Chih-Wei, Lu Richard, Iqbal Usman, Lin Shen-Hsien, Nguyen Phung Anh Alex, Yang Hsuan-Chia, Wang Chun-Fu, Li Jianping, Ma Kwan-Liu, Li Yu-Chuan Jack, Jian Wen-Shan
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
BMC Med Inform Decis Mak. 2015 Nov 12;15:92. doi: 10.1186/s12911-015-0218-7.
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.
电子病历（EMR）包含大量对医生、临床研究人员和医学政策制定者极具价值的数据。随着电子病历的规模、复杂性和可访问性不断增加，从其中提取有意义信息的能力已成为一个日益重要的待解决问题。
我们开发了一个标准化的数据分析流程，以支持针对特定疾病的队列研究。我们采用交互式分治方法将患者分类为每组相对统一的群体。这是一个重复过程，使用户能够将数据划分为可直观检查、比较和完善的同类子集。最终的可视化由转换后的数据驱动，用户反馈直接传达给相应的操作人员，从而完成重复过程。输出结果以桑基图样式的时间线显示，桑基图是一种特殊的流程图，用于展示因素随时间的状态和转变。
本文展示了一个基于网络的、视觉丰富的交互式应用程序，它能使研究人员利用电子病历数据对任何队列进行长期研究。生成的可视化有助于揭示数据中隐藏的信息，比较患者群体之间的差异，确定影响特定疾病的关键因素，并有助于指导进一步的分析。我们通过使用14567名慢性肾脏病（CKD）患者的电子病历介绍并演示了该工具。
我们开发了一个视觉挖掘系统，以支持对多维分类电子病历数据的探索性数据分析。以CKD作为疾病模型，它通过自动相关分析和人工策划的视觉评估组合而成。诸如桑基图之类的可视化方法可以揭示有关特定疾病队列以及该疾病随时间变化轨迹的有用知识。
