Aligning temporal data by sentinel events: discovering patterns in electronic health records

By T. Wang, Catherine Plaisant, Alexander J. Quinn, Roman Stanchak, Shawn N. Murphy, Ben Shneiderman. International Conference on Human Factors in Computing Systems, 2008

303 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/88659a25acb87f61fe5e3139ffe7786477955a4e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Neil T. Spring
2. Ben Shneiderman
3. Isaac S. Kohane
4. Ryen W. White
5. Tak-Chung Fu
6. J. C. Roberts

In Computer Vision and Pattern Recognition

1. Shixia Liu
2. Yingcai Wu
3. Mengchen Liu
4. Ben Shneiderman
5. Catherine Plaisant
6. Neil T. Spring
7. Kenyon Crowley
8. Fan Du

In Signal Processing

1. Shixia Liu
2. Yingcai Wu
3. Enxun Wei
4. Mengchen Liu
5. Panpan Xu
6. Neil T. Spring
7. Liu Ren
8. Ben Shneiderman

Citations in the corpus

• Sequence Synopsis: Optimize Visual Summary of Temporal Event Data (2018, background): “Lifelines2 [50, 51] supports interactive alignment of data on selected events that the users can easily spot precursor, co-occurring, and aftereffect events.”
• StoryFlow: Tracking the Evolution of Stories (2013, background): “Researchers have proposed many timeline visualizations such as LifeLines [31, 32] and its extension [44], PlanningLines [4], TimeLine [7], TIARA [23,24], RankExplorer [37], EventRiver [25], and SIMILE [2] to facilitate understanding event sequences.”
• Exploratory Search: Beyond the Query-Response Paradigm (2009, methodology): “Projects such as Lifelines2 (Wang et al., 2008) have used larger sets of data from patients’ electronic health records and medical test results, enabling medical professionals to align-rank and sort them according to the attributes available on the data.”
• EventAction (2019, background): “LifeLines2 [79] extends this approach to multiple records but does not scale well when displaying a large number of records in a stacked manner.”
• Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2) (2010, methodology): “Of particular interest is a temporal alignment query tool that directs temporal queries of the clinical data.(17) Using the i2b2 web client, patient sets may be created and aggregate counts on the demographics of these patients can be obtained.”
• Temporal Event Sequence Simplification (2013, background): “…of gaining understanding on two different levels: • Intra-Record Understanding: Knowledge is gained about the sequence of events that comprises a single record (i.e. a patient record or a shipment record): Why was this patient transferred from the Emergency Room to the Intensive Care Unit (ICU)?”
• LifeFlow: visualizing an overview of event sequences (2011, methodology): “events (in the same approach as LifeLines2 [34]).”