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COVID-19 has challenged the healthcare systems worldwide. To quickly identify successful diagnostic and therapeutic approaches large data sharing approaches are inevitable. Though organizational clinical data are abundant, many of them are available only in isolated silos and largely inaccessible to external researchers. To overcome and tackle this challenge the university medicine network (comprising all 36 German university hospitals) has been founded in April 2020 to coordinate COVID-19 action plans, diagnostic and therapeutic strategies and collaborative research activities. 13 projects were initiated from which the CODEX project, aiming at the development of a Germany-wide Covid-19 Data Exchange Platform, is presented in this publication. We illustrate the conceptual design, the stepwise development and deployment, first results and the current status.

The COVID-19 pandemic has caused strains on health systems worldwide disrupting routine hospital services for all non-COVID patients. Within this retrospective study, we analyzed inpatient hospital admissions across 18 German university hospitals during the 2020 lockdown period compared to 2018. Patients admitted to hospital between January 1 and May 31, 2020 and the corresponding periods in 2018 and 2019 were included in this study. Data derived from electronic health records were collected and analyzed using the data integration center infrastructure implemented in the university hospitals that are part of the four consortia funded by the German Medical Informatics Initiative. Admissions were grouped and counted by ICD 10 chapters and specific reasons for treatment at each site. Pooled aggregated data were centrally analyzed with descriptive statistics to compare absolute and relative differences between time periods of different years. The results illustrate how care process adoptions depended on the COVID-19 epidemiological situation and the criticality of the disease. Overall inpatient hospital admissions decreased by 35% in weeks 1 to 4 and by 30.3% in weeks 5 to 8 after the lockdown announcement compared to 2018. Even hospital admissions for critical care conditions such as malignant cancer treatments were reduced. We also noted a high reduction of emergency admissions such as myocardial infarction (38.7%), whereas the reduction in stroke admissions was smaller (19.6%). In contrast, we observed a considerable reduction in admissions for non-critical clinical situations, such as hysterectomies for benign tumors (78.8%) and hip replacements due to arthrosis (82.4%). In summary, our study shows that the university hospital admission rates in Germany were substantially reduced following the national COVID-19 lockdown. These included critical care or emergency conditions in which deferral is expected to impair clinical outcomes. Future studies are needed to delineate how appropriate medical care of critically ill patients can be maintained during a pandemic.

Objective: This paper introduces the objectives, methods and results of ontology development in the EU co-funded project Advancing Clinico-genomic Trials on Cancer – Open Grid Services for Improving Medical Knowledge Discovery (ACGT). While the available data in the life sciences has recently grown both in amount and quality, the full exploitation of it is being hindered by the use of different underlying technologies, coding systems, category schemes and reporting methods on the part of different research groups. The goal of the ACGT project is to contribute to the resolution of these problems by developing an ontology-driven, semantic grid services infrastructure that will enable efficient execution of discovery-driven scientific workflows in the context of multi-centric, post-genomic clinical trials. The focus of the present paper is the ACGT Master Ontology (MO). Methods: ACGT project researchers undertook a systematic review of existing domain and upper-level ontologies, as well as of existing ontology design software, implementation methods, and end-user interfaces. This included the careful study of best practices, design principles and evaluation methods for ontology design, maintenance, implementation, and versioning, as well as for use on the part of domain experts and clinicians. Results: To date, the results of the ACGT project include (i) the development of a master ontology (the ACGT-MO) based on clearly defined principles of ontology development and evaluation; (ii) the development of a technical infrastructure (the ACGT Platform) that implements the ACGT-MO utilizing independent tools, components and resources that have been developed based on open architectural standards, and which includes an application updating and evolving the ontology efficiently in response to end-user needs; and (iii) the development of an Ontology-based Trial Management Application (ObTiMA) that integrates the ACGT-MO into the design process of clinical trials in order to guarantee automatic semantic integration without the need to perform a separate mapping process.

Genetic dispositions play a major role in individual disease risk and treatment response. Genomic medicine, in which medical decisions are refined by genetic information of particular patients, is becoming increasingly important. Here we describe our work and future visions around the creation of a distributed infrastructure for pharmacogenetic data and medical decision support, based on industry standards such as the Web Ontology Language (OWL) and the Arden Syntax.

The challenges regarding seamless integration of distributed, heterogeneous and multilevel data arising in the context of contemporary, post-genomic clinical trials cannot be effectively addressed with current methodologies. An urgent need exists to access data in a uniform manner, to share information among different clinical and research centers, and to store data in secure repositories assuring the privacy of patients. Advancing Clinico-Genomic Trials (ACGT) was a European Commission funded Integrated Project that aimed at providing tools and methods to enhance the efficiency of clinical trials in the -omics era. The project, now completed after four years of work, involved the development of both a set of methodological approaches as well as tools and services and its testing in the context of real-world clinico-genomic scenarios. This paper describes the main experiences using the ACGT platform and its tools within one such scenario and highlights the very promising results obtained.

Thesauri that are “ontologized” into OWL-DL semantics are highly amenable to modeling errors resulting from falsely interpreting existential restrictions. We investigated the OWL-DL representation of the NCI Thesaurus (NCIT) in order to assess the correctness of existential restrictions. A random sample of 354 axioms using the someValuesFrom operator was taken. According to a rating performed by two domain experts, roughly half of these examples, and in consequence more than 76,000 axioms in the OWL-DL version, make incorrect assertions if interpreted according to description logics semantics. These axioms therefore constitute a huge source for unintended models, rendering most logic-based reasoning unreliable. After identifying typical error patterns we discuss some possible improvements. Our recommendation is to either amend the problematic axioms in the OWL-DL formalization or to consider some less strict representational format.

Clinical Trial Management Systems promise to help researchers in managing the large amounts of data occurring in clinical trials. In such systems Case Report Forms for capturing all patient data can usually be defined freely for a given trial. But if database definitions are automatically derived from such trial-specific definitions then the collected data cannot be easily compared to or integrated into other trials. We address this interoperability issue with an approach based on ontology and semantic data mediation. This resulted in the development of the ObTiMA system which is composed of a component for setting-up clinical trials and another for handling patient data during trials. Both components offer data reusability by relying on shared concepts defined in an ontology covering the whole cancer care and research spectrum.

Ontologies are more and more used in clinical informatics in different settings and supporting different functionalities. Most experts see the role of ontologies as operating in a black box and being invisible for the end-user. With respect to some of the systems that have recently been developed this is only partly possible. Therefore, we provide a methodology to create an end-user perspective on a clinical ontology. This will foster participation of the clinical expert in both ontology exploitation and ontology maintenance. This methodology does not only provide the basis for a better integration of the experts into the ontology-based system, but it can be used to support patient empowerment by helping the patient to understand the content that is stored and partake in its management.

Summary Objectives: The application of upper ontologies has been repeatedly advocated for to support the interoperability between different domain ontologies for facilitating the shared use of data within and across disciplines. BioTop is an upper domain ontology that aims at aligning more specialized biomolecular and biomedical ontologies. The integration of BioTop and the upper ontology Basic Formal Ontology (BFO) is the objective of this study. Methods: BFO was manually integrated into BioTop, observing both its free text and formal definitions. BioTop classes were attached to BFO classes as children and BFO classes were reused in the formal definitions of BioTop classes. A description logics reasoner was used to check the logical consistency of this integration. The domain adequacy was checked manually by domain experts. Results: Logical inconsistencies were found by the reasoner when applying the BFO classes for fiat and aggregated objects in some of the BioTop class definitions. We discovered that the definition of those particular classes in BFO was dependent on the notion of physical connectedness. Hence we suggest ignoring a BFO subbranch in order not to hinder cross-granularity integration. Conclusion: Without introducing a more sophisticated theory of granularity, the described problems cannot be properly dealt with. Whereas we argue that an upper ontology should be granularity-independent, we illustrate how granularity-dependent domain ontologies can still be embedded into the framework of BioTop in combination with BFO.

Motivation: The classification of biological entities in terms of species and taxa is an important endeavor in biology. Although a large amount of statements encoded in current biomedical ontologies is taxon-dependent there is no obvious or standard way for introducing taxon information into an integrative ontology architecture, supposedly because of ongoing controversies about the ontological nature of species and taxa. Results: In this article, we discuss different approaches on how to represent biological taxa using existing standards for biomedical ontologies such as the description logic OWL DL and the Open Biomedical Ontologies Relation Ontology. We demonstrate how hidden ambiguities of the species concept can be dealt with and existing controversies can be overcome. A novel approach is to envisage taxon information as qualities that inhere in biological organisms, organism parts and populations. Availability: The presented methodology has been implemented in the domain top-level ontology BioTop, openly accessible at http://purl.org/biotop. BioTop may help to improve the logical and ontological rigor of biomedical ontologies and further provides a clear architectural principle to deal with biological taxa information. Contact: stschulz@uni-freiburg.de

The application of upper ontologies has been repeatedly advocated for supporting interoperability between domain ontologies in order to facilitate shared data use both within and across disciplines. We have developed BioTop as a top-domain ontology to integrate more specialized ontologies in the biomolecular and biomedical domain. In this paper, we report on concrete integration problems of this ontology with the domain-independent Basic Formal Ontology (BFO) concerning the issue of fiat and aggregated objects in the context of different granularity levels. We conclude that the third BFO level must be ignored in order not to obviate cross-granularity integration.

The @neurIST ontology is currently under development within the scope of the European project @neurIST intended to serve as a module in a complex architecture aiming at providing a better understanding and management of intracranial aneurysms and subarachnoid hemorrhages. Due to the integrative structure of the project the ontology needs to represent entities from various disciplines on a large spatial and temporal scale. Initial term acquisition was performed by exploiting a database scaffold, literature analysis and communications with domain experts. The ontology design is based on the DOLCE upper ontology and other existing domain ontologies were linked or partly included whenever appropriate (e.g., the FMA for anatomical entities and the UMLS for definitions and lexical information). About 2300 predominantly medical entities were represented but also a multitude of biomolecular, epidemiological, and hemodynamic entities. The usage of the ontology in the project comprises terminological control, text mining, annotation, and data mediation.