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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2021 IEEE Workshop on Visual Analytics in Healthcare (VAHC) s. 19 - 24
Hlavní autori: Borland, David, Brain, Irena, Fecho, Karamarie, Pfaff, Emily, Xu, Hao, Champion, James, Bizon, Chris, Gotz, David
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.01.2021
Cornell University
Predmet:
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
ISSN:2331-8422, 2331-8422
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Working Paper/Pre-Print-1
ObjectType-Feature-3
content type line 23
ISSN:2331-8422
2331-8422
DOI:10.1109/VAHC53616.2021.00008