Information
- Publication Type: Master Thesis
- Workgroup(s)/Project(s): not specified
- Date: October 2022
- Date (Start): 2021
- Date (End): 2022
- Diploma Examination: 12. October 2022
- Open Access: yes
- First Supervisor: Renata Raidou
- Pages: 163
- Keywords: Visual Analytics, Automatic Image Segmentation, COVID-19, Life and medical sciences, Human-centered computing
Abstract
We propose 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 through chest X-ray images. 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 Saltz et al. [2021], 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 three different user groups (namely, medical and clinical experts, experts in data analytics, and the general population) through an interactive dashboard. The dashboard is designed to enable users to solve user-specific tasks, also defined in this work. Preliminary results indicate that the prediction results are improved by combining medical image data with clinical data, while analysis and communication of hospitalization outcomes prove to be a wide and significant topic in the context of COVID-19 prevention.
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BibTeX
@mastersthesis{Stritzel2022_thesis,
title = "Predicting and Communicating Outcome of COVID-19
Hospitalizations with Medical Image and Clinical Data",
author = "Oliver Stritzel",
year = "2022",
abstract = "We propose 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
through chest X-ray images. 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 Saltz et
al. [2021], 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 three different user groups
(namely, medical and clinical experts, experts in data
analytics, and the general population) through an
interactive dashboard. The dashboard is designed to enable
users to solve user-specific tasks, also defined in this
work. Preliminary results indicate that the prediction
results are improved by combining medical image data with
clinical data, while analysis and communication of
hospitalization outcomes prove to be a wide and significant
topic in the context of COVID-19 prevention.",
month = oct,
pages = "163",
address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
school = "Research Unit of Computer Graphics, Institute of Visual
Computing and Human-Centered Technology, Faculty of
Informatics, TU Wien",
keywords = "Visual Analytics, Automatic Image Segmentation, COVID-19,
Life and medical sciences, Human-centered computing",
URL = "https://www.cg.tuwien.ac.at/research/publications/2022/Stritzel2022_thesis/",
}
Master thesis
