LFPeers: Temporal Similarity Search in Covid-19 Data
International EuroVis Workshop on Visual Analytics (EuroVA) <2021, Online>
While there is a wide variety of visualizations and dashboards to help understand the data of the Covid-19 pandemic, hardly any of these support important analytical tasks, especially of temporal attributes. In this paper, we introduce a general concept for the analysis of temporal and multimodal data and the system LFPeers that applies this concept to the analysis of countries in a Covid-19 dataset. Our concept divides the analysis in two phases: a search phase to find the most similar objects to a target object before a time point t0, and an exploration phase to analyze this subset of objects after t0. LFPeers targets epidemiologists and the public who want to learn from the Covid-19 pandemic and distinguish successful and ineffective measures.
Web-based Prostate Visualization Tool
Proceedings of the 2020 Annual Meeting of the German Society of Biomedical Engineering
Jahrestagung der Deutschen Gesellschaft für Biomedizinische Technik im VDE (BMT) <54, 2020, online>
Current Directions in Biomedical Engineering
Proper treatment of prostate cancer is essential toincrease the survival chance. In this sense, numerous studiesshow how important the communication between all stakeholders in the clinic is. This communication is difficult because of the lack of conventions while referring to the locationwhere a biopsy for diagnosis was taken. This becomes evenmore challenging taking into account that experts of differentfields work on the data and have different requirements. In thispaper a web-based communication tool is proposed that incorporates a visualization of the prostate divided into 27 segments according to the PI-RADS protocol. The tool provides2 working modes that consider the requirements of radiologistand pathologist while keeping it consistent. The tool comprisesall relevant information given by pathologists and radiologists,such as, severity grades of the disease or tumor length. Everything is visualized using a colour code for better undestanding.
Self-Service Data Preprocessing and Cohort Analysis for Medical Researchers
2019 IEEE Workshop on Visual Analytics in Healthcare
IEEE Workshop on Visual Analytics in Healthcare (VAHC) <10, 2019, Vancouver, BC, Canada>
Medical researchers are increasingly interested in data-driven approaches to support informed decisions in many medical areas. They collect data about the patients they treat, often creating their own specialized data tables with more characteristics than what is defined in their clinical information system (CIS). Usually, these data tables or sEHR (small electronical health records) are rather small, maybe containing the data of only hundreds of patients. Medical researchers are struggling to find an easy way to first clean and transform these sEHR, and then create cohorts and perform confirmative or exploratory analysis. This paper introduces a methodology and identifies requirements for building systems for self-service data preprocessing and cohort analysis for medical researchers. We also describe a system based on this methodology and the requirements that shows the benefits of our approach. We further highlight these benefits with an example scenario from our projects with clinicians specialized on head&neck cancer treatment.