Müller, JulianeCypko, MarioOeser, AlexanderStoehr, MatthäusZebralla, VeitSchreiber, StefanieWiegand, SusanneDietz, AndreasOeltze-Jafra, SteffenOeltze-Jafra, Steffen and Raidou, Renata Georgia2021-06-122021-06-122021978-3-03868-147-2https://doi.org/10.2312/evm.20211075https://diglib.eg.org:443/handle/10.2312/evm20211075Clinical decision-making for complex diseases such as cancer aims at finding the right diagnosis, optimal treatment or best aftercare for a specific patient. The decision-making process is very challenging due to the distributed storage of patient information entities in multiple hospital information systems, the required inclusion of multiple clinical disciplines with their different views of disease and therapy, and the multitude of available medical examinations, therapy options and aftercare strategies. Clinical Decision Support Systems (CDSS) address these difficulties by presenting all relevant information entities in a concise manner and providing a recommendation based on interdisciplinary disease- and patient-specific models of diagnosis and treatment. This work summarizes our research on visual assistance for therapy decision-making. We aim at supporting the preparation and implementation of expert meetings discussing cancer cases (tumor boards) and the aftercare consultation. In very recent work, we started to address the generation of models underlying a CDSS. The developed solutions combine state-of-the-art interactive visualizations with methods from statistics, machine learning and information organization.Human centered computingVisual analyticsApplied computingLife and medical sciences10.2312/evm.202110757-11