LifeFlow

Wongsuphasawat, Krist; Gomez, John Alexis Guerra; Plaisant, Catherine; Wang, Taowei David; Taieb-Maimon, Meirav; Shneiderman, Ben

doi:10.1145/1978942.1979196

Cited by 225 publications

(15 citation statements)

References 44 publications

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“…Their system outputs visual-oriented summary information to show the prevalence of the events as well as to allow comparison between multiple groups of events [ 26 ]. For overview specific tasks, Wongsuphasawat et al proposed LifeFlow, a novel visualization that simplifies and aggregates temporal event sequences into a tree-based visual summary [ 27 ]. Monroe et al improved the usability of the system by integrating interval-based events and developing a set of user-driven simplification techniques in conjunction with a metric for measuring visual complexity [ 13 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

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

Huang

Iqbal

et al. 2015

BMC Med Inform Decis Mak

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BackgroundElectronic 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.MethodsWe 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.ResultsThis 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.ConclusionsWe 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.

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Section: Discussionmentioning

confidence: 99%

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

Huang

Iqbal

et al. 2015

BMC Med Inform Decis Mak

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“…As we have discussed, aligning temporal event sequences by sentinel events of interest can help display patterns of precursor, co-occurring, and aftereffect events [13]. Single-event alignment was pioneered by LifeLines2 [55] for clinical tasks and has been adopted in the design of, e.g., CareCruiser [21], LifeFlow [60], EventFlow [32], and Peekquence [28]. Single-event alignment has since been applied to many diverse domains [13].…”

Section: Temporal Event Sequence Visualizationmentioning

confidence: 99%

“…In order to provide overviews of the entire set of sequences, several tools have used layered flow graph visualizations. LifeFlow [60] and EventFlow [32] have used hierarchical icicle plots to aggregate events at multiple layers out from the aligned sentinel event. BaobabView [53] used a comparable approach for visualizing decision trees.…”

Section: Temporal Event Sequence Visualizationmentioning

confidence: 99%

Sequence Braiding: Visual Overviews of Temporal Event Sequences and Attributes

Bartolomeo

Zhang

Sheng

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: An example of SEQUENCE BRAIDING applied to blood glucose and meal data of a patient with type 1 diabetes. This temporal event sequence and attribute data is modeled using a layered directed acyclic network, where each node represents an individual event (meal) and contiguous edges connect the events from a single day in sequence. Nodes are aligned into columns based on their event (meal) type and into rows based on attribute values, in this case quantitative blood glucose groups. Nodes are also color-coded by attribute group: very high, high, normal, low, and very low.

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“…There are also ways to improve the sense making in event sequences with primarily visual design. For example, LifeFlow (Wongsuphasawat et al 2011) and CoCo (Malik et al 2015) summarize low carnality patient journey events with a tree-structured view. MatrixWave (Zhao et al 2015) achieved the summarization by mostly layout design, which is appealing because direct visual representation without the loss of information is proven to be highly effective.…”

Section: Techniques In Summarizing Event Sequencementioning

confidence: 99%

Visualizing event sequence game data to understand player’s skill growth through behavior complexity

Funk

et al. 2019

J Vis

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Analysis of game data is used to study player behavior. For puzzle-based games where solutions are usually defined by their action sequences, player behavior can also be studied by their solution complexity. In this paper, we present a visualization system to help learning expert to understand how actions, timing and the resulting strategy change with regard to the solution complexity. To establish a novel perspective into the patterns not only in action choices but also in behavior complexity, we designed an interactive, customized line chart to track how complexity and performance change at each stage of skill acquisition. Specialized glyph system (Strategy Signature) is implemented to find strategy differences easily with simple visual cues. Contextual information can be explored by switching the view modes to see potential links between complexity and raw attributes. Evaluation with expert users shows that the system effectively reduced their time and effort in finding interesting subgroups and gave them unexplored angles of behavior complexity to contemplate player's skill growth. In summary, this paper illustrates a visualization approach to enable analysis into the subtleties of behavior complexity in video games.

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LifeFlow

Cited by 225 publications

References 44 publications

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

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

Sequence Braiding: Visual Overviews of Temporal Event Sequences and Attributes

Visualizing event sequence game data to understand player’s skill growth through behavior complexity

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