Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2015

Open Access 01-12-2015 | Research article

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

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

Published in: BMC Medical Informatics and Decision Making | Issue 1/2015

Login to get access

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.
Literature
1.
Miller RH, Sim I. Physicians’ use of electronic medical records: barriers and solutions. Health Aff. 2004;23(2):116–26.CrossRef
2.
Caban JJ, Gotz D. Visual analytics in healthcare–opportunities and research challenges. J Am Med Inform Assoc. 2015;22(2):260–2.CrossRefPubMed
3.
Cook KA, Thomas JJ. Illuminating the path: The research and development agenda for visual analytic. Richland: Pacific Northwest National Laboratory (PNNL); 2005.
4.
Lex A, Schulz H, Streit M, Partl C, Schmalstieg D. VisBricks: multiform visualization of large, inhomogeneous data. IEEE Trans Vis Comput Graph. 2011;17(12):2291–300.CrossRefPubMed
5.
Basole RC, Braunstein ML, Kumar V, Park H, Kahng M, Chau DH, et al. Understanding variations in pediatric asthma care processes in the emergency department using visual analytics. J Am Med Inform Assoc. 2015;22(2):318–23. doi:10.​1093/​jamia/​ocu016.CrossRefPubMed
6.
Jianping Li C-FW, Kwan-Liu M. Design considerations for visualizing large EMR data, EHRVis - visualizing electronic health record data. Paris: EEE VIS 2014 WORKSHO; 2014.
7.
Huang C-W, Syed-Abdul S, Jian W-S, Iqbal U, Nguyen P-AA, Lee P, et al. A novel tool for visualizing chronic kidney disease associated polymorbidity: a 13-year cohort study in Taiwan. J Am Med Inform Assoc. 2015;22(2):290–8.CrossRefPubMed
8.
Ochiai A. Zoogeographic studies on the soleoid fishes found in Japan and its neighbouring regions. Bull Jpn Soc Sci Fish. 1957;22(9):526–30.CrossRef
9.
Wongsuphasawat K, Gotz D. Outflow: visualizing patient flow by symptoms and outcome, IEEE VisWeek workshop on visual analytics in healthcare. Providence: IEEE; 2011. p. 2011.
10.
Kosara R, Bendix F, Hauser H. Parallel sets: Interactive exploration and visual analysis of categorical data. IEEE Trans Vis Comput Graph. 2006;12(4):558–68.CrossRefPubMed
11.
Riehmann P, Hanfler M, Froehlich B. Interactive sankey diagrams, Information visualization, 2005 INFOVIS 2005 IEEE symposium on: 2005. Minneapolis: IEEE; 2005. p. 233–40.
12.
Ellis G, Dix A. A taxonomy of clutter reduction for information visualisation. IEEE Trans Vis Comput Graph. 2007;13(6):1216–23.CrossRefPubMed
13.
Monroe M, Wongsuphasawat K, Plaisant C, Shneiderman B, Millstein J, Gold S. Exploring point and interval event patterns: display methods and interactive visual query. Univ Maryland Tech Rep. 2012.
14.
Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012;13(6):395–405.CrossRefPubMed
15.
Tufte ER, Graves-Morris P. The visual display of quantitative information. Cheshire: Graphics press; 1983.
16.
Cousins SB, Kahn MG. The visual display of temporal information. Artif Intell Med. 1991;3(6):341–57.CrossRef
17.
Plaisant C, Mushlin R, Snyder A, Li J, Heller D, Shneiderman B. LifeLines: using visualization to enhance navigation and analysis of patient records, Proceedings of the AMIA symposium: 1998. Bethesda: American Medical Informatics Association; 1998. p. 76.
18.
Nair V, Kaduskar M, Bhaskaran P, Bhaumik S, Lee H. Preserving narratives in electronic health records, Bioinformatics and biomedicine (BIBM), 2011 IEEE international conference on: 2011. Atlanta: IEEE; 2011. p. 418–21.
19.
Bui AA, Aberle DR, Kangarloo H. TimeLine: visualizing integrated patient records. IEEE Trans Inf Technol Biomed. 2007;11(4):462–73.CrossRefPubMed
20.
Park H, Choi J. V-model: a new innovative model to chronologically visualize narrative clinical texts, Proceedings of the SIGCHI conference on human factors in computing systems: 2012. New York: ACM; 2012. p. 453–62.
21.
Bade R, Schlechtweg S, Miksch S. Connecting time-oriented data and information to a coherent interactive visualization, Proceedings of the SIGCHI conference on human factors in computing systems: 2004. New York: ACM; 2004. p. 105–12.
22.
Hochheiser H, Shneiderman B. Dynamic query tools for time series data sets: timebox widgets for interactive exploration. Inf Vis. 2004;3(1):1–18.CrossRef
23.
Fails JA, Karlson A, Shahamat L, Shneiderman B. A visual interface for multivariate temporal data: finding patterns of events across multiple histories, Visual analytics science and technology, 2006 IEEE symposium on: 2006. Baltimore: IEEE; 2006. p. 167–74.
24.
Jin J, Szekely P. Interactive querying of temporal data using a comic strip metaphor, Visual analytics science and technology (VAST), 2010 IEEE symposium on: 2010. Salt Lake: IEEE; 2010. p. 163–70.
25.
Wang TD, Plaisant C, Quinn AJ, Stanchak R, Murphy S, Shneiderman B. Aligning temporal data by sentinel events: discovering patterns in electronic health records, Proceedings of the SIGCHI conference on human factors in computing systems: 2008. New York: ACM; 2008. p. 457–66.
26.
Wang TD, Plaisant C, Shneiderman B, Spring N, Roseman D, Marchand G, et al. Temporal summaries: supporting temporal categorical searching, aggregation and comparison. IEEE Trans Vis Comput Graph. 2009;15(6):1049–56.CrossRefPubMed
27.
Wongsuphasawat K, Guerra Gómez JA, Plaisant C, Wang TD, Taieb-Maimon M, Shneiderman B. LifeFlow: visualizing an overview of event sequences, Proceedings of the SIGCHI conference on human factors in computing systems: 201. New York: ACM; 2011. p. 1747–56.
28.
Monroe M, Lan R, Lee H, Plaisant C, Shneiderman B. Temporal event sequence simplification. IEEE Trans Vis Comput Graph. 2013;19(12):2227–36.CrossRefPubMed
29.
Wongsuphasawat K, Gotz D. Exploring flow, factors, and outcomes of temporal event sequences with the outflow visualization. IEEE Trans Vis Comput Graph. 2012;18(12):2659–68.CrossRefPubMed
30.
Turkay C, Lex A, Streit M, Pfister H, Hauser H. Characterizing cancer subtypes using dual analysis in caleydo stratomex. IEEE Trans Vis Comput Graph. 2014;34(2):38.CrossRef
31.
Piringer H, Buchetics M. Exploring proportions: comparative visualization of categorical data, Visual analytics science and technology (VAST), 2011 IEEE conference on: 2011. Providence: IEEE; 2011. p. 295–6.
Metadata
Title
A richly interactive exploratory data analysis and visualization tool using electronic medical records
Authors
Chih-Wei Huang
Richard Lu
Usman Iqbal
Shen-Hsien Lin
Phung Anh (Alex) Nguyen
Hsuan-Chia Yang
Chun-Fu Wang
Jianping Li
Kwan-Liu Ma
Yu-Chuan (Jack) Li
Wen-Shan Jian
Publication date
01-12-2015
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2015
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-015-0218-7

Other articles of this Issue 1/2015

BMC Medical Informatics and Decision Making 1/2015 Go to the issue

Research article

Cultural adaptation of a shared decision making tool with Aboriginal women: a qualitative study

Research article

Logic regression-derived algorithms for syndromic management of vaginal infections

Research article

Data mining EEG signals in depression for their diagnostic value

Research article

Decision coaching using the Ottawa family decision guide with parents and their children: a field testing study

Research article

A scoping review of cloud computing in healthcare

Research article

Translation and psychometric properties of the Chinese version of the Leeds Attitudes to Concordance II scale