Intelligent selection and retrieval of multiple time-oriented records

Klimov, Denis; Shaḥar, Yuval; Taieb-Maimon, Meirav

doi:10.1007/s10844-009-0100-0

Cited by 28 publications

(23 citation statements)

References 31 publications

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“…This system is embedded in a specific architecture including a temporal database, Chronus II [14], providing a temporal query language. KNAVE-II [15,16] and VISITORS [9,17] are exploration tools based on the RESUME system for TAs extraction. KNAVE-II provides a complex architecture including a knowledge-based visualization and exploration module, a domain ontology server and a temporal-database mediator, and is oriented to visualizing information related to single patients.…”

Section: Related Workmentioning

confidence: 99%

JTSA: An open source framework for time series abstractions

Sacchi

Capozzi²,

Bellazzi

et al. 2015

Computer Methods and Programs in Biomedicine

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Section: Related Workmentioning

confidence: 99%

JTSA: An open source framework for time series abstractions

Sacchi

Capozzi²,

Bellazzi

et al. 2015

Computer Methods and Programs in Biomedicine

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“…Our interactive visual interface is developed with the guidelines from this publication in mind. The VISITORS system [28,29] has an interactive visual interface to explore and query patient data over time. It supports multi-variate data and combines both actual values and events.…”

Section: Samples For Application Of Interactive Visual Interfaces In mentioning

confidence: 99%

Interactive Visual Transformation for Symbolic Representation of Time-Oriented Data

Lammarsch

Aigner

Bertone³

et al. 2013

Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data

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Data Mining on time-oriented data has many real-world applications, like optimizing shift plans for shops or hospitals, or analyzing traffic or climate. As those data are often very large and multi-variate, several methods for symbolic representation of time-series have been proposed. Some of them are statistically robust, have a lower-bound distance measure, and are easy to configure, but do not consider temporal structures and domain knowledge of users. Other approaches, proposed as basis for Apriori pattern finding and similar algorithms, are strongly configurable, but the parametrization is hard to perform, resulting in ad-hoc decisions. Our contribution combines the strengths of both approaches: an interactive visual interface that helps defining event classes by applying statistical computations and domain knowledge at the same time. We are not focused on a particular application domain, but intend to make our approach useful for any kind of time-oriented data.

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“…They generally focus on supporting physicians to quickly absorb a patient's potentially lengthy medical history in order to make better medical decisions. On the other hand, a number of systems expand the coverage to multiple EHRs, for example, Similan (13) , Protempa (14) , Gravi++ (15) , VISITORS (16) , and IPBC (17) . These systems typically focus on novel search and aggregation strategies for multiple EHRs.…”

Section: Related Workmentioning

confidence: 99%

“…We discovered that depending on the focus of a medical scenario, sometimes our collaborators reasoned at a higher abstraction (categories), and sometimes lower (numerical values), and sometimes the abstractions change within the same scenario. Most visualization systems focus on either categorical data or numerical data, but there are a few systems that visualize machine-created abstractions (14,16) . Our experiences suggest that a visualization system that supports temporal analysis seamlessly in multiple, userdriven, and dynamically constructed, abstractions will be valuable.…”

Section: (Support Flexible Data Types) Some Of the Earlier Case Studimentioning

confidence: 99%

Extracting Insights from Electronic Health Records: Case Studies, a Visual Analytics Process Model, and Design Recommendations

et al. 2011

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Current electronic health record (EHR) systems facilitate the storage, retrieval, persistence, and sharing of patient data. However, the way physicians interact with EHRs has not changed much. More specifically, support for temporal analysis of a large number of EHRs has been lacking. A number of information visualization techniques have been proposed to alleviate this problem. Unfortunately, due to their limited application to a single case study, the results are often difficult to generalize across medical scenarios. We present the usage data of Lifelines2 [22], our information visualization system, and user comments, both collected over eight different medical case studies. We generalize our experience into a visual analytics process model for multiple EHRs. Based on our analysis, we make seven design recommendations to information visualization tools to explore EHR systems.

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Intelligent selection and retrieval of multiple time-oriented records

Cited by 28 publications

References 31 publications

JTSA: An open source framework for time series abstractions

JTSA: An open source framework for time series abstractions

Interactive Visual Transformation for Symbolic Representation of Time-Oriented Data

Extracting Insights from Electronic Health Records: Case Studies, a Visual Analytics Process Model, and Design Recommendations

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