A richly interactive exploratory data analysis and visualization tool using electronic medical records

BMC Medical Informatics and Decision Making

Research Article

Jianping Li¹  Kwan-Liu Ma¹  Chun-Fu Wang¹  Usman Iqbal²  Shen-Hsien Lin²  Chih-Wei Huang²  Phung Anh (Alex) Nguyen²  Richard Lu²  Yu-Chuan (Jack) Li³  Hsuan-Chia Yang⁴  Wen-Shan Jian⁵ 

[1] Department of Computer Science, University of California-Davis, Davis, CA, USA;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;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;Department of Dermatology, Wan-Fang Hospital, Taipei, Taiwan;International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan;Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan;International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan;School of Health Care Administration, Taipei Medical University, Taipei, Taiwan;Faculty of Health Sciences, Macau University of Science and Technology, Macau, China;

关键词: Systemic Lupus Erythematosus;    Chronic Kidney Disease;    Time Window;    Chronic Kidney Disease Patient;    Exploratory Data Analysis;   

DOI  :  10.1186/s12911-015-0218-7

 received in 2015-04-20, accepted in 2015-11-02,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundElectronic 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.MethodsWe 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.ResultsThis 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.ConclusionsWe 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.

【 授权许可】

CC BY   
© Huang et al. 2015

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