Predicting, Analyzing and Communicating Outcomes of COVID-19 Hospitalizations with Medical Images and Clinical Data

Simple item page
dc.contributor.authorStritzel, Oliveren_US
dc.contributor.authorRaidou, Renata Georgiaen_US
dc.contributor.editorRenata G. Raidouen_US
dc.contributor.editorBjörn Sommeren_US
dc.contributor.editorTorsten W. Kuhlenen_US
dc.contributor.editorMichael Kroneen_US
dc.contributor.editorThomas Schultzen_US
dc.contributor.editorHsiang-Yun Wuen_US
dc.date.accessioned2022-09-19T11:46:35Z
dc.date.available2022-09-19T11:46:35Z
dc.date.issued2022
dc.description.abstractWe propose PACO, a visual analytics framework to support the prediction, analysis, and communication of COVID-19 hospitalization outcomes. Although several real-world data sets about COVID-19 are openly available, most of the current research focuses on the detection of the disease. Until now, no previous work exists on combining insights from medical image data with knowledge extracted from clinical data, predicting the likelihood of an intensive care unit (ICU) visit, ventilation, or decease. Moreover, available literature has not yet focused on communicating such results to the broader society. To support the prediction, analysis and communication of the outcomes of COVID-19 hospitalizations on the basis of a publicly available data set comprising both electronic health data and medical image data [SSP∗21], we conduct the following three steps: (1) automated segmentation of the available X-ray images and processing of clinical data, (2) development of a model for the prediction of disease outcomes and a comparison to state-of-the-art prediction scores for both data sources, i.e., medical images and clinical data, and (3) the communication of outcomes to two different groups (i.e., clinical experts and the general population) through interactive dashboards. Preliminary results indicate that the prediction, analysis and communication of hospitalization outcomes is a significant topic in the context of COVID-19 prevention.en_US
dc.description.sectionheadersVisual Analytics, Artificial Intelligence
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20221196
dc.identifier.isbn978-3-03868-177-9
dc.identifier.issn2070-5786
dc.identifier.pages129-133
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/vcbm.20221196
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20221196
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visual Analytics; Applied computing → Life and medical sciences"
dc.subjectHuman centered computing → Visual Analytics
dc.subjectApplied computing → Life and medical sciences"
dc.titlePredicting, Analyzing and Communicating Outcomes of COVID-19 Hospitalizations with Medical Images and Clinical Dataen_US
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
129-133.pdf
Size:
1.82 MB
Format:
Adobe Portable Document Format
Download
Thumbnail Image
Name:
paper1022_mm.pdf
Size:
355.56 KB
Format:
Adobe Portable Document Format
Download
Collections
VCBM 2022: Eurographics Workshop on Visual Computing for Biology and Medicine