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Harmonisation of German Health Care Data Using the OMOP Common Data Model – A Practice Report
(2023)
Data harmonization is an important step in large-scale data analysis and for generating evidence on real world data in healthcare. With the OMOP common data model, a relevant instrument for data harmonization is available that is being promoted by different networks and communities. At the Hannover Medical School (MHH) in Germany, an Enterprise Clinical Research Data Warehouse (ECRDW) is established and harmonization of that data source is the focus of this work. We present MHH’s first implementation of the OMOP common data model on top of the ECRDW data source and demonstrate the challenges concerning the mapping of German healthcare terminologies to a standardized format.
Autonomous and integrated passenger and freight transport (APFIT) is a promising approach to tackle both, traffic and last-mile-related issues such as environmental emissions, social and spatial conflicts or operational inefficiencies. By conducting an agent-based simulation, we shed light on this widely unexplored research topic and provide first indications regarding influential target figures of such a system in the rural area of Sarstedt, Germany. Our results show that larger fleets entail inefficiencies due to suboptimal utilization of monetary and material resources and increase traffic volume while higher amounts of unused vehicles may exacerbate spatial conflicts. Nevertheless, to fit the given demand within our study area, a comparatively large fleet of about 25 vehicles is necessary to provide reliable service, assuming maximum passenger waiting times of six minutes to the expense of higher standby times, rebalancing effort, and higher costs for vehicle acquisition and maintenance.
The NOA project collects and stores images from open access publications and makes them findable and reusable. During the project a focus group workshop was held to determine whether the development is addressing researchers’ needs. This took place before the second half of the project so that the results could be considered for further development since addressing users’ needs is a big part of the project. The focus was to find out what content and functionality they expect from image repositories.
In a first step, participants were asked to fill out a survey about their images use. Secondly, they tested different use cases on the live system. The first finding is that users have a need for finding scholarly images but it is not a routine task and they often do not know any image repositories. This is another reason for repositories to become more open and reach users by integrating with other content providers. The second finding is that users paid attention to image licenses but struggled to find and interpret them while also being unsure how to cite images. In general, there is a high demand for reusing scholarly images but the existing infrastructure has room to improve.
Building a well-founded understanding of the concepts, tasks and limitations of IT in all areas of society is an essential prerequisite for future developments in business and research. This applies in particular to the healthcare sector and medical research, which are affected by the noticeable advances in digitization. In the transfer project “Zukunftslabor Gesundheit” (ZLG), a teaching framework was developed to support the development of further education online courses in order to teach heterogeneous groups of learners independent of location and prior knowledge. The study at hand describes the development and components of the framework.
Powder bed-based additive manufacturing processes offer an extended freedom in design and enable the processing of metals, ceramics, and polymers with a high level of relative density. The latter is a prevalent measure of process and component quality, which depends on various input variables. A key point in this context is the condition of powder beds. To enhance comprehension of their particle-level formation and facilitate process optimization, simulations based on the Discrete Element Method are increasingly employed in research. To generate qualitatively as well as quantitatively reliable simulation results, an adaptation of the contact model parameterization is necessary. However, current adaptation methods often require the implementation of models that significantly increase computational effort, therefore limiting their applicability. To counteract this obstacle, a sophisticated formula-based adaptation and evaluation method is presented in this research. Additionally, the developed method enables accelerated parameter determination with limited experimental effort. Thus, it represents an integrative component, which supports further research efforts based on the Discrete Element Method by significantly reducing the parameterization effort. The universal nature of deducting this method also allows its adaptation to similar parameterization problems and its implementation in other fields of research.
Pathologists need to identify abnormal changes in tissue. With the developing digitalization, the used tissue slides are stored digitally. This enables pathologists to annotate the region of interest with the support of software tools. PathoLearn is a web-based learning platform explicitly developed for the teacher-student scenario, where the goal is that students learn to identify potential abnormal changes. Artificial intelligence (AI) and machine learning (ML) have become very important in medicine. Many health sectors already utilize AI and ML. This will only increase in the future, also in the field of pathology. Therefore, it is important to teach students the fundamentals and concepts of AI and ML early in their studies. Additionally, creating and training AI generally requires knowledge of programming and technical details. This thesis evaluates how this boundary can be overcome by comparing existing end-to-end AI platforms and teaching tools for AI. It was shown that a visual programming editor offers a fitting abstraction for creating neural networks without programming. This was extended with real-time collaboration to enable students to work in groups. Additionally, an automatic training feature was implemented, removing the necessity to know technical details about training neural networks.
After kidney transplantation graft rejection must be prevented. Therefore, a multitude of parameters of the patient is observed pre- and postoperatively. To support this process, the Screen Reject research project is developing a data warehouse optimized for kidney rejection diagnostics. In the course of this project it was discovered that important information are only available in form of free texts instead of structured data and can therefore not be processed by standard ETL tools, which is necessary to establish a digital expert system for rejection diagnostics. Due to this reason, data integration has been improved by a combination of methods from natural language processing and methods from image processing. Based on state-of-the-art data warehousing technologies (Microsoft SSIS), a generic data integration tool has been developed. The tool was evaluated by extracting Banff-classification from 218 pathology reports and extracting HLA mismatches from about 1700 PDF files, both written in german language.
In this poster we present the ongoing development of an integrated free and open source toolchain for semantic annotation of digitised cultural heritage. The toolchain development involves the specification of a common data model that aims to increase interoperability across diverse datasets and to enable new collaborative research approaches.
This paper aims to provide a structured overview of four open, participatory formats that are particularly applicable in inquiry-based teaching and learning contexts: hackathons, book sprints, barcamps, and learning circles. Using examples, mostly from the work and experience context of the Open Science Lab at TIB Hannover, we address concrete processes, working methods, possible outcomes and challenges.
The compilation offers an introduction to the topic and is intended to provide tools for testing in practice.
Techno-economic analysis that allocate costs to the energy flows of energy systems are helpful to understand the formation of costs within processes and to increase the cost efficiency. For the economic evaluation, the usefulness or quality of the energy is of great importance. In exergy-based methods, this is considered by allocating costs to the exergy instead of energy. As exergy represents the ability of performing work, it is often named the useful part of energy. In contrast, the anergy, the part of energy, which cannot perform work, is often assumed to be not useful.
However, heat flows as used e.g. in domestic heating are always a mixture of a relative small portion of exergy and a big portion of anergy. Although of lower quality, the anergy is obviously useful for these applications. The question is, whether it makes sense to differentiate between exergy and anergy and take both properties into account for the economic evaluation.
To answer this question, a new methodical concept based on the definition of an anergy-exergy cost ratio is compared to the commonly applied approaches of considering either energy or exergy as the basis for economic evaluation. These three different approaches for the economic analysis of thermal energy systems are applied to an exemplary heating system with thermal storages. It is shown that the results of the techno-economic analysis can be improved by giving anergy an economic value and that the proposed anergy-cost ratio allows a flexible adaptation of the evaluation depending on the economic constraints of a system.