In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.

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The assessment of patient well-being is highly relevant for the early detection of diseases, for assessing the risks of therapies, or for evaluating therapy outcomes. The knowledge to assess a patient's well-being is actually tacit knowledge and thus, can only be used by the physicians themselves. The rationale of this research approach is to use visual interfaces to capture the mental models of experts and make them available more explicitly. We present a visual active learning system that enables physicians to label the well-being state of patient histories suffering prostate cancer. The labeled instances are iteratively learned in an active learning approach. In addition, the system provides models and visual interfaces for a) estimating the number of patients needed for learning, b) suggesting meaningful learning candidates and c) visual feedback on test candidates. We present the results of two evaluation strategies that prove the validity of the applied model. In a representative real-world use case, we learned the feedback of physicians on a data collection of more than 16.000 prostate cancer histories

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In this paper we present NetCapVis, an web-based progressive visual analytics system where the user can upload PCAP files, set initial filters to reduce the data before uploading and then instantly interact with the data while the rest is progressively loaded into the visualizations.