Appropriate data models are essential for the systematic collection, aggregation, and integration of health data and for subsequent analysis. However, recommendations for modeling health data are often not publicly available within specific projects. Therefore, the project Zukunftslabor Gesundheit investigates recommendations for modeling. Expert interviews with five experts were conducted and analyzed using qualitative content analysis. Based on the condensed categories “governance”, “modeling” and “standards”, the project team generated eight hypotheses for recommendations on health data modeling. In addition, relevant framework conditions such as different roles, international cooperation, education/training and political influence were identified. Although emerging from interviewing a small convenience sample of experts, the results help to plan more extensive data collections and to create recommendations for health data modeling.
Due to demographic change the number of serious kidney diseases and thus required transplantations will increase. The increased demand for donor organs and a decreasing supply of these organs underline the necessity for effective early rejection diagnostic measures to improve the lifetime of transplants. Expert systems might improve rejection diagnostics but for the development of such systems data models are needed that encompass the relevant information to enable optimal data aggregation and evaluation. Results of a literature review concerning published data models and information systems concerned with kidney transplant rejection diagnostic lead to a set of data elements even if no papers could be identified that publish data models explicitly.
Das Forschungscluster Smart Data Analytics stellt in dem vorliegenden Band seine Forschung aus den Jahren 2019 und 2020 vor. In der ersten Hälfte des Bandes geben 20 Kurzporträts von laufenden oder kürzlich abgeschlossenen Projekten einen Überblick über die Forschungsthemen im Cluster. Enthalten in den Kurzporträts ist eine vollständige, kommentierte Liste der wissenschaftlichen Veröffentlichungen aus den Jahren 2019 und 2020. In der zweiten Hälfte dieses Bandes geben vier längere Beiträge exemplarisch einen tieferen Einblick in die Forschung des Clusters und behandeln Themen wie Fehlererkennung in Datenbanken, Analyse und Visualisierung von Sicherheitsvorfällen in Netzwerken, Wissensmodellierung und Datenintegration in der Medizin, sowie die Frage ob ein Computerprogramm Urheber eines Kunstwerkes im Sinne des Urheberrechts sein kann.