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EventFlow: Visual Analysis of Temporal Event Sequences
and Advanced Strategies for Healthcare Discovery

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EventFlow: Advanced Strategies for Healthcare Discovery

While Eventflow has been used in a variety of other application domains (cybersecurity, sports analytics, incident management etc.) the analysis of healthcare data has been our driving application domain.

With Eventflow health providers, insurance companies, and hospitals now have a powerful tool for discovering patterns in Electronic Health Records and claims reports. EventFlow displays point and interval events in
(1) a visually comprehensible format for each patient, as well as in
(2) a novel aggregation view that reveals common and rare patterns across the entire database.

The innovative graphical user interfaces offers powerful search capabilities that enable users to specify temporal patterns. The ranked result sets using the visually comprehensible format for each patient. Search & Replace features, plus rich manipulations enable users to transform their datasets so as to confirm required treatment patterns, detect missing events, and reveal unexpected sequences.

Medical researchers have already applied EventFlow to analyze treatment patterns and outcomes, while network security analysts have studied cyberattack patters and sports analysts have found novel approaches to studying games, overall team performance, and seasonal patterns. Applications for web log, sensor data, business processes, and financial transactions constitute secondary markets.

Project History

The HCIL's ongoing work with temporal event records has produced powerful tools for analyzing and exploring patterns of point-based events (Lifelines2, LifeFlow). However, users found that point-based events limited their capacity to solve problems that had inherently interval attributes, for example, the 3-month interval during which patients took a medication. To address this issue, EventFlow extends its predecessors to support both point-based and interval-based events. Interval-based events represent a fundamental increase in complexity at every level of the application, from the input and data structure to the eventual questions that a user might ask of the data. Our goal was to accomplish this integration in a way that appeared to users as a simple and intuitive extension of the original LifeFlow tool. With EventFlow, we present novel solutions for displaying interval events, simplifying their visual impact, and incorporating them into meaningful queries.


  • Megan Monroe, PhD Candidate, Computer Science
  • Sana Malik, PhD Candidate, Computer Science
  • Fan Du, PhD Candidate, Computer Science
  • Christopher Imbriano , PhD Candidate, Computer Science
  • Catherine Plaisant, Research Scientist, UMIACS
  • Ben Shneiderman, Professor, Computer Science
  • Jeff Millstein, Oracle Research
  • Past Participants
  • Rongjian Lan, PhD Candidate, Computer Science
  • Krist Wongsuphasawat PhD, Computer Science
  • Sigfried Gold, Social & Scientific Systems
  • Collaborators
  • We appreciate the collaboration of clinical researchers and epidemiologists at the US Army Pharmacovigilance Center, University of Maryland School of Pharmacy, National Children Hospital, University of Florida, Washington Hospital Center, and many others.


We appreciate the partial support of Oracle Corporation and the Center for Health-related Informatics and Bioimaging (CHIB) at the University of Maryland. EventFlow builds on early work which was funded in part by NIH - National Cancer Institute grant RC1-CA147489 "Interactive Exploration of Temporal Patterns in Electronic Health Records" (for LifeLines2 and LifeFlow), and later by the Maryland Industrial Partnerships (MIPS) program and Pulse8.


If you are in a rush, this video provides a good overview of how the EventFlow aggregation is constructed, and how the search and replace can be used to manipulate the data to answer questions.

Or: take a look at the analysis of BASKETBALL data: Basketball Play-by-play Analysis

The Offensive Rebounding of the Indiana Pacers

Or see an older demo but still useful because it stats with a different example dataset
(exploring patient paths after they enter the Emergency Room - e.g. looking for bounce back)


The following slides provide an introduction to the motivation behind EventFlow, and a summary of its features.
A full description can be found in the tech report.

Other Presentations

Temporal Event Querying - Presented at CHI2013
Dataset Simplification - Presented at MedStar 2/13
Basketball Play-by-play Analysis - Presented at the 2013 HCIL Symposium

Download & Licensing

We can provide you with a review version of Eventflow

  • For non-commercial use: please contact eventflow.umd@gmail.com with a description of your project and organization)
  • For commercial use: EventFlow is available for licensing. To request a review copy of EventFlow and for more information about licensing please contact:
    Office of Technology Commercialization (OTC)
    2130 Mitchell Building, University of Maryland, College Park, MD 20742
    Phone: 301-405-3947 | Fax: 301-314-9502
    Email: umdtechtransfer@umd.edu
    URL: www.otc.umd.edu
  • Not sure: contact eventflow.umd@gmail.com

User Support


Full Papers

[BEST REFERENCE] Simplification of temporal event sequences:
Megan Monroe, Rongjian Lan, Catherine Plaisant, Ben Shneiderman
Temporal Event Sequence Simplification
In IEEE Trans. Visualization and Computer Graphics, 19, 12 (May 2013), 2227-36.

Interactive Querying
Megan Monroe, Rongjian Lan, Juan Morales del Olmo, Catherine Plaisant, Ben Shneiderman, and Jeff Millstein
The Challenges of Specifying Intervals and Absences in Temporal Queries: A Graphical Language Approach.
in Proc. Of ACM Conference on Human-Computer Interaction (CHI '2013), 2349-2358. October 2013.

Paper on temporal query processing algorithms
Megan Monroe, Amol Deshpande
An Integer Programming Approach to Temporal Pattern Matching Queries
Proc. of International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-13), 1028-1035. June 2013.

Short Papers and Technical Reports

Short "Viewpoints" paper summarizing the analysis strategies we observed:
Ben Shneiderman, Catherine Plaisant
Sharpening Analytic Focus to Cope with Big Data Volume and Variety: Ten strategies for data focusing with temporal event sequences
IEEE Computer Graphics and Applications, 35, 3 (2015) 10-14

Early work on temporal event sequence search and replace
Rongjian Lan, Hanseung Lee, Megan Monroe, Allan Fong, Catherine Plaisant, Ben Shneiderman
Temporal Search and Replace: An Interactive Tool for the Analysis of Temporal Event Sequences

Early report on extending the Lifeflow overview visualization to handle interval data
Megan Monroe, Krist Wongsuphasawat, Catherine Plaisant, Ben Shneiderman, Jeff Millstein and Sigfried Gold
Exploring Point and Interval Event Patterns: Display Methods and Interactive Visual Query

Case Studies

A case study of our collaboration effort with the US Army Pharmacovigilance Center:
Megan Monroe, Tamra Meyer, Catherine Plaisant, Rongjian Lan, Krist Wongsuphasawat, Trinka Coster
Sigfried Gold, Jeff Millstein, Ben Shneiderman
A Pilot Study of Asthma Medications in the Military Health System.
June 2013.

A much more detailed version of the case study:
Catherine Plaisant, Megan Monroe, Tamra Meyer, Ben Shneiderman.
Interactive Visualization
Chapter 12 of Big Data and Health Analytics, Katherine Marconi and Harold Lehman (Eds), CRC Press - Taylor and Francis (October 2014), 243-262.

Trauma Resuscitation
Carter, E., Burd, R., Monroe, M., Plaisant, C., Shneiderman, B.
Using EventFlow to Analyze Task Performance During Trauma Resuscitation
Proc. of the Workshop on Interactive Systems in Healthcare (WISH 2013). 2013.

Prescriptions adherence analysis
Bjarnadottir, M., Malik, S., Onukwugha, E., Gooden, T., Plaisant, C. (October 2015)
Understanding Adherence and Prescription Patterns Using Large Scale Claims Data
To appear in PharmacoEconomics

Other publications written by our case study partners

E. Onukwugha, Y. Kwok, C. Yong, C. Mullins, B. Seal, A. Hussain,
Variation in the length of radiation therapy among men diagnosed with Incident Metastatic Prostate Cancer"
Poster presented at the 2013 ASTRO (American Society for Radiation Ocology)meeting.

See also our 2014 HCIL Workshop on Visualization of Temporal Patterns in EHR data:, that included several presentations from EventFlow users.

General survey paper

Rind, A., Wang, T., Aigner, W., Miksch, S., Wongsuphasawat, K., Plaisant, C., Shneiderman, B., Interactive Information Visualization for Exploring and Querying Electronic Health Records: A Systematic Review, in Foundations and Trends in Human-Computer Interaction, Vol. 5, No. 3 (2013) 207-298.


Hunter Whitney, It's About Time, UX Magazine, September 2014.

Products and papers stimulated by this work

Our original research on LifeFlow and EventFlow stimulated new work by other labs, such as:

Related projects and events