Enabling Longitudinal Exploratory Analysis of Clinical COVID Data
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...
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| Veröffentlicht in: | 2021 IEEE Workshop on Visual Analytics in Healthcare (VAHC) S. 19 - 24 |
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| Format: | Tagungsbericht Journal Article |
| Sprache: | Englisch |
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IEEE
01.01.2021
Cornell University |
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| ISSN: | 2331-8422, 2331-8422 |
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| 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. |
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| AbstractList | 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. 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. 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.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. |
| Author | Pfaff, Emily Xu, Hao Gotz, David Bizon, Chris Brain, Irena Fecho, Karamarie Borland, David Champion, James |
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| Snippet | 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... 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... |
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| SubjectTerms | Collaboration Conferences COVID-19 Data visualization Human-centered computing [Visualization]: Visualization application domains-Visual analytics Human-centered computing [Visualization]: Visualization systems and tools human-computer interaction medical informatics Medical services Pandemics temporal event sequence visualization Visual analytics |
| Title | Enabling Longitudinal Exploratory Analysis of Clinical COVID Data |
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