Browsing by Author "Oeltze-Jafra, Steffen"

Now showing 1 - 8 of 8
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    Comparative Visualization of Longitudinal 24-hour Ambulatory Blood Pressure Measurements in Pediatric Patients with Chronic Kidney Disease
    (The Eurographics Association, 2023) Özmen, Mahmut; Jabarulla, Mohamed Yaseen; Grabitz, Carl Robert; Melk, Anette; Wühl, Elke; Oeltze-Jafra, Steffen; Gillmann, Christina; Krone, Michael; Lenti, Simone
    Pediatric chronic kidney disease (CKD) increases the risk of cardiovascular disease, stroke and other life-threatening conditions. Monitoring blood pressure in CKD patients is crucial to managing these risks. 24-hour ambulatory blood pressure monitoring (ABPM) is recommended for its comprehensive and accurate assessment of blood pressure over 24 hours. Analyzing and comparing 24-hour ABPM data of multiple diagnostic visits is a challenging task. Traditional methods involve comparing individual visits using paper printouts, which can be time-consuming and lacks a systematic overview of deviations over time. In this work, we present a dashboard visualization that allows clinicians (i) to assess the evolution of ABPM data over multiple diagnostic visits, (ii) to compare ABPM data of CKD patients with reference data of a healthy cohort, and (iii) to perform a detailed intra-individual comparison of the ABPM data acquired at two subsequent diagnostic visits. We demonstrate the dashboard in a case study of a patient with mild-to-moderate-stage CKD.
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    Comprehensive Visualization of Longitudinal Patient Data for the Dermatological Oncological Tumor Board
    (The Eurographics Association, 2020) Steinhauer, Nastasja; Hörbrugger, Marc; Braun, Andreas Dominik; Tüting, Thomas; Oeltze-Jafra, Steffen; Müller, Juliane; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
    In multidisciplinary oncological team meetings for patient-specific treatment decision-making, so-called tumor boards, usually one physician introduces a patient case verbally and proposes an initial therapy recommendation. This is followed by a short collaborative discussion of the recommendation's suitability. While patient-related image data, such as CT and MR scans, are displayed during the discussion, clinical patient data must be memorized from the introduction or repeatedly inquired by the participating domain experts. To support physicians in this concern, we propose a comprehensive visualization of longitudinal patient-specific information entities during case introduction and discussion. Our visual approach advances over existing work by simultaneously providing an overview of the current patient status as well as of previous therapy measures and their effects on the status. The latter assists in relating the currently proposed recommendation to the previous treatment measures and the related patient status. The visualization has been designed in close collaboration with dermatologists and oncologists aiming at a comprehensive yet easily comprehensible presentation of relevant patient-data and minimal user interaction. The usability and clinical relevance of the prototypical implementation of our visual approach have been evaluated in a qualitative user study with five domain experts based on real anonymized data of melanoma patients.
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    EUROGRAPHICS 2019: Dirk Bartz Prize Frontmatter
    (Eurographics Association, 2019) Bruckner, Stefan; Oeltze-Jafra, Steffen; Bruckner, Stefan and Oeltze-Jafra, Steffen
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    EuroVis 2020 CGF 39-3 STARs: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Smit, Noeska; Oeltze-Jafra, Steffen; Wang, Bei; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
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    EuroVis 2021 Dirk Bartz Prize: Frontmatter
    (The Eurographics Association, 2021) Oeltze-Jafra, Steffen; Raidou, Renata Georgia; Oeltze-Jafra, Steffen and Raidou, Renata Georgia
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    Interactive Visual Analysis of Patient-Reported Outcomes for Improved Cancer Aftercare
    (The Eurographics Association, 2019) Müller, Juliane; Zebralla, Veit; Wiegand, Susanne; Oeltze-Jafra, Steffen; Landesberger, Tatiana von and Turkay, Cagatay
    The monitoring and planning of cancer aftercare are commonly based on clinical, physiological and caregiver-reported outcome measures. More recently, patient-reported outcome (PRO) measures, capturing social, psychological, and financial aspects, are gaining attention in the course of establishing a patient-centered healthcare system. PROs are acquired during regular aftercare consultations where patients are asked to fill in questionnaires. We present an interactive visual analysis (IVA) approach to investigating PROs. The approach is applied in clinical routine during the aftercare consultation to assess the development of the particular patient, to compare this development to those of similar patients, and to detect trends that may require an adaptation of the aftercare strategy. Furthermore, the approach is employed in clinical research to identify groups of similarly developing patients and risk factors for poor outcomes, as well as to visually compare patient groups. We demonstrate the IVA approach in analyzing PROs of 1025 head and neck cancer patients. In an evaluation with 20 clinicians, we assessed the usefulness and usability of a prototypical implementation.
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    VCBM 2021: Frontmatter
    (The Eurographics Association, 2021) Oeltze-Jafra, Steffen; Smit, Noeska N.; Sommer, Björn; Nieselt, Kay; Schultz, Thomas; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
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    Visual Assistance in Clinical Decision Support
    (The Eurographics Association, 2021) Müller, Juliane; Cypko, Mario; Oeser, Alexander; Stoehr, Matthäus; Zebralla, Veit; Schreiber, Stefanie; Wiegand, Susanne; Dietz, Andreas; Oeltze-Jafra, Steffen; Oeltze-Jafra, Steffen and Raidou, Renata Georgia
    Clinical 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.