A richly interactive exploratory data analysis and visualization tool using electronic medical records Clinical decision-making, knowledge support systems, and theory

Chih Wei Huang, Richard Lu, Iqbal Usman, Shen Hsien Lin, Phung Anh Nguyen, Hsuan Chia Yang, Chun Fu Wang, Jianping Li, Kwan Liu Ma, Yu Chuan Li, Wen Shan Jian

研究成果: 雜誌貢獻文章同行評審

12 引文 斯高帕斯(Scopus)

摘要

Background: 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. Methods: 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. Results: 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. Conclusions: 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.
原文英語
文章編號92
期刊BMC Medical Informatics and Decision Making
15
發行號1
DOIs
出版狀態已發佈 - 十一月 12 2015

ASJC Scopus subject areas

  • Health Informatics
  • Health Policy

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