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  1. Article ; Online: Enabling Longitudinal Exploratory Analysis of Clinical COVID Data.

    Borland, David / Brain, Irena / Fecho, Karamarie / Pfaff, Emily / Xu, Hao / Champion, James / Bizon, Chris / Gotz, David

    ArXiv

    2021  

    Abstract: As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from ... ...

    Abstract As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from longitudinal clinical data, a team of collaborators worked to apply existing event sequence visual analytics technologies to a longitudinal clinical data from a cohort of 998 patients with high rates of COVID-19 infection. This paper describes the initial steps toward this goal, including: (1) the data transformation and processing work required to prepare the data for visual analysis, (2) initial findings and observations, and (3) qualitative feedback and lessons learned which highlight key features as well as limitations to address in future work.
    Language English
    Publishing date 2021-08-25
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Enabling Longitudinal Exploratory Analysis of Clinical COVID Data

    Borland, David / Brain, Irena / Fecho, Karamarie / Pfaff, Emily / Xu, Hao / Champion, James / Bizon, Chris / Gotz, David

    2021  

    Abstract: As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from ... ...

    Abstract As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from longitudinal clinical data, a team of collaborators worked to apply existing event sequence visual analytics technologies to a longitudinal clinical data from a cohort of 998 patients with high rates of COVID-19 infection. This paper describes the initial steps toward this goal, including: (1) the data transformation and processing work required to prepare the data for visual analysis, (2) initial findings and observations, and (3) qualitative feedback and lessons learned which highlight key features as well as limitations to address in future work.

    Comment: To Appear in Proceedings of Visual Analytics in Healthcare 2021
    Keywords Computer Science - Human-Computer Interaction
    Publishing date 2021-08-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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