Stritzel, OliverRaidou, Renata GeorgiaRenata G. RaidouBjörn SommerTorsten W. KuhlenMichael KroneThomas SchultzHsiang-Yun Wu2022-09-192022-09-192022978-3-03868-177-92070-5786https://doi.org/10.2312/vcbm.20221196https://diglib.eg.org:443/handle/10.2312/vcbm20221196We 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.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visual Analytics; Applied computing → Life and medical sciences"Human centered computing → Visual AnalyticsApplied computing → Life and medical sciences"10.2312/vcbm.20221196129-1335 pages