A Visual Interface for Multivariate Temporal Data: Finding Patterns of Events across Multiple Histories

Fails, Jerry Alan; Karlson, Amy; Shahamat, Layla; Shneiderman, Ben

doi:10.1109/vast.2006.261421

Cited by 126 publications

(75 citation statements)

References 15 publications

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“…The Results Panel in Figure 5 shows the results for a pattern that identifies patients whose cholesterol went down following two dosages of a blue pill. The results panel shows an overview of the matches using a dotand-line style [3], where each dot represents a matching event, and each line represents a temporal gap between events. It shows the complete set of matches so that users can quickly gain an overview of the matches at a glance, and see outliers or any patterns that develop within the matches.…”

Section: Results Panelmentioning

confidence: 99%

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Interactive querying of temporal data using a comic strip metaphor

Jin

Szekely

2010

2010 IEEE Symposium on Visual Analytics Science and Technology

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Finding patterns in temporal data is an important data analysis task in many domains. Static visualizations can help users easily see certain instances of patterns, but are not specially designed to support systematic analysis tasks, such as finding all instances of a pattern automatically. VizPattern is an interactive visual query environment that uses a comic strip metaphor to enable users to easily and quickly define and locate complex temporal patterns. Evaluations provide evidence that VizPattern is applicable in many domains, and that it enables a wide variety of users to answer questions about temporal data faster and with fewer errors than existing state-of-the-art visual analysis systems.

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Section: Results Panelmentioning

confidence: 99%

“…The language is expressive, but users studies show that the double-sided sliders are hard to use [16]. PatternFinder [3] [13] is also supports an expressive language to find patterns of events across multiple records. It uses forms to specify queries, and a balland-chain visualization to display matching results.…”

Section: Related Workmentioning

confidence: 99%

Interactive querying of temporal data using a comic strip metaphor

Jin

Szekely

2010

2010 IEEE Symposium on Visual Analytics Science and Technology

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“…PatternFinder (Fails et al 2006) provides tools for the user to query patterns by specifying the attributes of events and time spans.…”

Section: Related Workmentioning

confidence: 99%

Integrated visualisation of wearable sensor data and risk models for individualised health monitoring and risk assessment to promote patient empowerment

Zhao

Parvinzamir

Wilson

et al. 2016

J Vis

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Patient empowerment delivers health and social care services that enable people to gain more control of their healthcare needs. With the advancement of sensor technologies, it is increasingly possible to monitor people's health with dedicated wearable sensors. The consistent measurements from a variety of wearable sensors imply that a huge amount of data may be exploited to monitor and predict people's health using medically proven models. In the process of health data representation and analysis, visualisation can be employed to promote data analysis and knowledge discovery via mature visual paradigms and welldesigned user interactions. In this paper, we introduce the role of visualisation for individualised health monitoring and risk management in the background of a European Commission funded project, which aims to provide self-management of cardiorenal diseases with the assistance of wearable sensors. The DOI 10.1007/s12650-016-0402-6 visualisation components of health monitoring, risk model exploration, and risk analysis are presented to achieve personalised health and risk monitoring and to promote people's wellbeing. It allows the patients not only to view existing risks, but also to gain awareness of the right pathway to change their lifestyles in order to reduce potential health risks.

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“…The visualisation used by Fails et al can remind of an event chart showing multiple lines per history, one for each hit of a temporal query [22]. However, the visualisation shows only the time spanned by the search hits, as opposed to the traditional event chart showing the entire histories.…”

Section: ) Lifelinesmentioning

confidence: 99%

Visual exploration and cohort identification of acute patient histories aggregated from heterogeneous sources

Sætre¹,

Nytrø²,

Nordbo³

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW)

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Abstract-How can we use information visualization to support retrospective, exploratory analysis of collections of histories for patients admitted to acute care? This paper describes a novel design for visual cohort identification and exploration. We have developed a tool that integrates multiple, heterogeneous clinical data sources and allows alignment, querying and abstraction in a common workbench. This paper presents results from two projects and a review of related work in the field of information visualization including both presentation and interactive navigation of the information. We have developed an interactive prototype and present the visualization aspect of this prototype and a brief demonstration of its use in a research project with a large cohort of patients.The prototype represents and reasons with patient events in different OWL-formalizations according to the perspective and use: One for integration and alignment of patient records and observations; Another for visual presentation of individual or cohort trajectories.Health researchers have successfully analyzed large cohorts (over 100,000 individuals) using the tool. We have also used the tool to produce interactive personal health time-lines (for more than 10,000 individuals) on the web. Utility, usability and effect have been tested extensively and the results so far are promising.We envision that clinicians who want to learn more about groups of patients and their treatment processes will find the tool valuable. In addition, we believe that the visualization can be useful to researchers looking at data to be statistically evaluated, in order to discover new hypotheses or get ideas for the best analysis strategies. Our main conclusion is that the tool is usable, but it can be challenging to use for large data sets.

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A Visual Interface for Multivariate Temporal Data: Finding Patterns of Events across Multiple Histories

Cited by 126 publications

References 15 publications

Interactive querying of temporal data using a comic strip metaphor

Interactive querying of temporal data using a comic strip metaphor

Integrated visualisation of wearable sensor data and risk models for individualised health monitoring and risk assessment to promote patient empowerment

Visual exploration and cohort identification of acute patient histories aggregated from heterogeneous sources

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