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

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number92
JournalBMC Medical Informatics and Decision Making
Volume15
Issue number1
DOIs
StatePublished - Nov 12 2015

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Electronic Health Records
Chronic Renal Insufficiency
Cohort Studies
Research Personnel
Administrative Personnel
Clinical Decision-Making
Physicians

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics

Cite this

A richly interactive exploratory data analysis and visualization tool using electronic medical records Clinical decision-making, knowledge support systems, and theory. / Huang, Chih Wei; Lu, Richard; Iqbal, Usman; Lin, Shen Hsien; Nguyen, Phung Anh; Yang, Hsuan Chia; Wang, Chun Fu; Li, Jianping; Ma, Kwan-Liu; Li, Yu Chuan; Jian, Wen Shan.

In: BMC Medical Informatics and Decision Making, Vol. 15, No. 1, 92, 12.11.2015.

Research output: Contribution to journalArticle

Huang, Chih Wei ; Lu, Richard ; Iqbal, Usman ; Lin, Shen Hsien ; Nguyen, Phung Anh ; Yang, Hsuan Chia ; Wang, Chun Fu ; Li, Jianping ; Ma, Kwan-Liu ; Li, Yu Chuan ; Jian, Wen Shan. / A richly interactive exploratory data analysis and visualization tool using electronic medical records Clinical decision-making, knowledge support systems, and theory. In: BMC Medical Informatics and Decision Making. 2015 ; Vol. 15, No. 1.
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AU - Nguyen, Phung Anh

AU - Yang, Hsuan Chia

AU - Wang, Chun Fu

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