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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.

Journal of biomedical semantics 11(1), 6 (2020). doi:10.1186/s13326-020-00223-z, Published by BioMed Central, London

Data Intelligence 2(1/2), 96-107 (2020). doi:10.1162/dint_a_00032 special issue: "Special Issue: Emerging FAIR Practices / Issue Editors: Barend Mons, Erik Schultes, Annika Jacobsen", Published by MIT Press, Cambridge, MA

Nowadays personalized medicine is of increasing importance, especially in the field of cancer therapy. More and more hospitals are conducting molecular tumor boards (MTBs) bringing together experts from various fields with different expertise to discuss patient cases taking into account genetic information from sequencing data. Yet, there is still a lack of tools to support collaborative exploration and decision making. To fill this gap, we developed a novel user interface to support MTBs. A task analysis of MTBs currently held at German hospitals showed, that there is less collaborative exploration during the meeting as expected, with a large part of the information search being done during the MTB preparation. Thus we designed our interface to support both situations, a single user preparing the MTB and the presentation of information and group discussion during the meeting.

Sustainability of project output and especially of the maintenance and further development of software is of growing concern for the research community. In the personalized medicine project p-medicine solutions that address this sustainability problem were developed and discussed in a workshop. They involve a number of interrelated and mutually supportive measures including the creation of a service center, building modular software, using common data standards, mutual service exchange with a research infrastructure, Open Source and fee-based software provision, joint promotion and deployment of tools in a regulated, clinical trial situation. These ideas join a nascent literature seeking to understand how project output can be put into a sustainable environment and to suggest solutions that may be useful for academic projects in general.

The challenge of modelling cancer presents a major opportunity to improve our ability to reduce mortality from malignant neoplasms, improve treatments and meet the demands associated with the individualization of care needs. This is the central motivation behind the ContraCancrum project. By developing integrated multi-scale cancer models, ContraCancrum is expected to contribute to the advancement of in silico oncology through the optimization of cancer treatment in the patient-individualized context by simulating the response to various therapeutic regimens. The aim of the present paper is to describe a novel paradigm for designing clinically driven multi-scale cancer modelling by bringing together basic science and information technology modules. In addition, the integration of the multi-scale tumour modelling components has led to novel concepts of personalized clinical decision support in the context of predictive oncology, as is also discussed in the paper. Since clinical adaptation is an inelastic prerequisite, a long-term clinical adaptation procedure of the models has been initiated for two tumour types, namely non-small cell lung cancer and glioblastoma multiforme; its current status is briefly summarized.

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.

In the life sciences, there is an ample need for semantic interoperability of data. Thus shared vocabularies are needed for consistently expressing metadata in terms of semantic annotations as well as for querying bibliographic information systems. In the past years, lots of highly specialized, yet also fragmented terminologies have evolved. However, they lack principled forms of conceptual interlinkage. In order to provide an ontological basis for a seamless integration of such isolated parts of biological knowledge, we here introduce BioTop, an upper domain ontology for molecular biology. We describe its structure and contents, as well as its current interfaces to a selected set of OBO ontologies, which contain more detailed terminological knowledge about specific areas of molecular biology, e.g., cell types, molecular functions, biological processes and chemical compounds.

Semantic interoperability is essential to facilitate efficient collaboration in heterogeneous multi-site healthcare environments. The deployment of a semantic interoperability solution has the potential to enable a wide range of informatics supported applications in clinical care and research both within a single healthcare organization and in a network of organizations. At the same time, building and deploying a semantic interoperability solution may require significant effort to carry out data transformation and to harmonize the semantics of the information in the different systems. Our approach to semantic interoperability leverages existing healthcare standards and ontologies, focusing first on specific clinical domains and key applications, and gradually expanding the solution when needed. An important objective of this work is to create a semantic link between clinical research and care environments to enable applications such as streamlining the execution of multi-centric clinical trials, including the identification of eligible patients for the trials. This paper presents an analysis of the suitability of several widely-used medical ontologies in the clinical domain: SNOMED-CT, LOINC, MedDRA, to capture the semantics of the clinical trial eligibility criteria, of the clinical trial data (e.g., Clinical Report Forms), and of the corresponding patient record data that would enable the automatic identification of eligible patients. Next to the coverage provided by the ontologies we evaluate and compare the sizes of the sets of relevant concepts and their relative frequency to estimate the cost of data transformation, of building the necessary semantic mappings, and of extending the solution to new domains. This analysis shows that our approach is both feasible and scalable.

Usability testing methods are nowadays integrated into the design and development of health-care software, and the need for usability in health-care information technology (IT) is widely accepted by clinicians and researchers. Usability assessment starts with the identification of specific objectives that need to be tested and continues with the definition of evaluation criteria and monitoring procedures before usability tests are performed to assess the quality of all services and tasks. Such a process is implemented in the p-medicine environment and gives feedback iteratively to all software developers in the project. GCP (good clinical practice) criteria require additional usability testing of the software. For the p-medicine project (www.p-medicine.eu), an extended usability concept (EUC) was developed. The EUC covers topics like ease of use, likeability, and usefulness, usability in trial centres characterised by a mixed care and research environment and by extreme time constraints, confidentiality, use of source documents, standard operating procedures (SOA), and quality control during data handling to ensure that all data are reliable and have been processed correctly in terms of accuracy, completeness, legibility, consistence, and timeliness. Here, we describe the p-medicine EUC, focusing on two of the many key tools: ObTiMA and the Ontology Annotator (OA).

Improving the initial diagnosis and the assessment of response to treatment in malignant gliomas, while avoiding invasive methods as much as justifiable, is one major aspect actual research is focusing on. Imaging studies are used to calculate tumor volume and define vital, necrotic and cystic areas within a tumor. Though the visual interpretation of magnetic resonance (MR) images is based on qualitative observation of variation in signal intensity, a correlation of signal intensities with histological features of a tumor is not possible. Better methods are needed for a reliable interpretation of follow-up studies in single patients. Histograms of signal intensities might serve as a method adding quantitative data to the description of a tumor. Using DoctorEye software, tumors can be easily rendered and histograms of the signal intensities within a tumor as well as mean and median signal intensities are possible to calculate. Our results in glioblastoma suggest that these histograms are an innovative method of gaining new tumor-specific information without performing additional investigations in a patient. It can be an additional diagnostic tool in differentiating various intracranial lesions from each other, as well as in assessing response to treatment or progression of malignant glioma.

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.