Schäfer, SteffenBaumgartl, TomWulff, AntjeKuijper, ArjanMarschollek, MichaelScheithauer, Simonevon Landesberger, TatianaKrone, MichaelLenti, SimoneSchmidt, Johanna2022-06-022022-06-022022978-3-03868-185-4https://doi.org/10.2312/evp.20221113https://diglib.eg.org:443/handle/10.2312/evp20221113We present a novel visual-interactive interface to show results of a machine learning algorithm, which predicts the infection probability for patients in hospitals. The model result data is complex and needs to be presented in a clear and intuitive way to microbiology and infection control experts in hospitals. Our visual-interactive interface offers linked views which allow for detailed analysis of the model results. Feedback from microbiology and infection control experts showed that they were able to extract new insights regarding outbreaks and transmission pathways.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing --> Visual analytics; Computing methodologies --> Artificial intelligenceHuman centered computingVisual analyticsComputing methodologiesArtificial intelligenceInteractive Visualization of Machine Learning Model Results Predicting Infection Risk10.2312/evp.2022111331-333 pages