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The German Centre for Cardiovascular Research (DZHK) is one of the German Centres for Health Research and aims to conduct early and guideline-relevant studies to develop new therapies and diagnostics that impact the lives of people with cardiovascular disease. Therefore, DZHK members designed a collaboratively organised and integrated research platform connecting all sites and partners. The overarching objectives of the research platform are the standardisation of prospective data and biological sample collections among all studies and the development of a sustainable centrally standardised storage in compliance with general legal regulations and the FAIR principles. The main elements of the DZHK infrastructure are web-based and central units for data management, LIMS, IDMS, and transfer office, embedded in a framework consisting of the DZHK Use and Access Policy, and the Ethics and Data Protection Concept. This framework is characterised by a modular design allowing a high standardisation across all studies. For studies that require even tighter criteria additional quality levels are defined. In addition, the Public Open Data strategy is an important focus of DZHK. The DZHK operates as one legal entity holding all rights of data and biological sample usage, according to the DZHK Use and Access Policy. All DZHK studies collect a basic set of data and biosamples, accompanied by specific clinical and imaging data and biobanking. The DZHK infrastructure was constructed by scientists with the focus on the needs of scientists conducting clinical studies. Through this, the DZHK enables the interdisciplinary and multiple use of data and biological samples by scientists inside and outside the DZHK. So far, 27 DZHK studies recruited well over 11,200 participants suffering from major cardiovascular disorders such as myocardial infarction or heart failure. Currently, data and samples of five DZHK studies of the DZHK Heart Bank can be applied for.

Electrocardiography analysis is widely used in various clinical applications and Deep Learning models for classification tasks are currently in the focus of research. Due to their data-driven character, they bear the potential to handle signal noise efficiently, but its influence on the accuracy of these methods is still unclear. Therefore, we benchmark the influence of four types of noise on the accuracy of a Deep Learning-based method for atrial fibrillation detection in 12-lead electrocardiograms. We use a subset of a publicly available dataset (PTB-XL) and use the metadata provided by human experts regarding noise for assigning a signal quality to each electrocardiogram. Furthermore, we compute a quantitative signal-to-noise ratio for each electrocardiogram. We analyze the accuracy of the Deep Learning model with respect to both metrics and observe that the method can robustly identify atrial fibrillation, even in cases signals are labelled by human experts as being noisy on multiple leads. False positive and false negative rates are slightly worse for data being labelled as noisy. Interestingly, data annotated as showing baseline drift noise results in an accuracy very similar to data without. We conclude that the issue of processing noisy electrocardiography data can be addressed successfully by Deep Learning methods that might not need preprocessing as many conventional methods do.

Contradictions as a data quality indicator are typically understood as impossible combinations of values in interdependent data items. While the handling of a single dependency between two data items is well established, for more complex interdependencies, there is not yet a common notation or structured evaluation method established to our knowledge. For the definition of such contradictions, specific biomedical domain knowledge is required, while informatics domain knowledge is responsible for the efficient implementation in assessment tools. We propose a notation of contradiction patterns that reflects the provided and required information by the different domains. We consider three parameters (α, β, θ): the number of interdependent items as α, the number of contradictory dependencies defined by domain experts as β, and the minimal number of required Boolean rules to assess these contradictions as θ. Inspection of the contradiction patterns in existing R packages for data quality assessments shows that all six examined packages implement the (2,1,1) class. We investigate more complex contradiction patterns in the biobank and COVID-19 domains showing that the minimum number of Boolean rules might be significantly lower than the number of described contradictions. While there might be a different number of contradictions formulated by the domain experts, we are confident that such a notation and structured analysis of the contradiction patterns helps to handle the complexity of multidimensional interdependencies within health data sets. A structured classification of contradiction checks will allow scoping of different contradiction patterns across multiple domains and effectively support the implementation of a generalized contradiction assessment framework.

The project “Collaboration on Rare Diseases” CORD-MI connects various university hospitals in Germany to collect sufficient harmonized electronic health record (EHR) data for supporting clinical research in the field of rare diseases (RDs). However, the integration and transformation of heterogeneous data into an interoperable standard through Extract-Transform-Load (ETL) processes is a complex task that may influence the data quality (DQ). Local DQ assessments and control processes are needed to ensure and improve the quality of RD data. We therefore aim to investigate the impact of ETL processes on the quality of transformed RD data. Seven DQ indicators for three independent DQ dimensions were evaluated. The resulting reports show the correctness of calculated DQ metrics and detected DQ issues. Our study provides the first comparison results between the DQ of RD data before and after ETL processes. We found that ETL processes are challenging tasks that influence the quality of RD data. We have demonstrated that our methodology is useful and capable of evaluating the quality of real-world data stored in different formats and structures. Our methodology can therefore be used to improve the quality of RD documentation and to support clinical research.

The COVID-19 pandemic has urged the need to set up, conduct and analyze high-quality epidemiological studies within a very short time-scale to provide timely evidence on influential factors on the pandemic, e.g. COVID-19 severity and disease course. The comprehensive research infrastructure developed to run the German National Pandemic Cohort Network within the Network University Medicine is now maintained within a generic clinical epidemiology and study platform NUKLEUS. It is operated and subsequently extended to allow efficient joint planning, execution and evaluation of clinical and clinical-epidemiological studies. We aim to provide high-quality biomedical data and biospecimens and make its results widely available to the scientific community by implementing findability, accessibility, interoperability and reusability – i.e. following the FAIR guiding principles. Thus, NUKLEUS might serve as role model for FAIR and fast implementation of clinical epidemiological studies within the setting of University Medical Centers and beyond.

The need to harness large amounts of data, possibly within a short period of time, became apparent during the Covid-19 pandemic outbreak. In 2022, the Corona Data Exchange Platform (CODEX), which had been developed within the German Network University Medicine (NUM), was extended by a number of common components, including a section on FAIR science. The FAIR principles enable research networks to evaluate how well they comply with current standards in open and reproducible science. To be more transparent, but also to guide scientists on how to improve data and software reusability, we disseminated an online survey within the NUM. Here we present the outcomes and lessons learnt.

Background Digitalization is playing a major role in mastering the current coronavirus 2019 (COVID-19) pandemic. However, several outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in German hospitals last year have shown that many of the surveillance and warning mechanisms related to infection control (IC) in hospitals need to be updated. Objectives The main objective of the following work was to assess the state of information technology (IT) systems supporting IC and surveillance in German university hospitals in March 2021, almost a year into the SARS-CoV-2 pandemic. Methods As part of the National Research Network for Applied Surveillance and Testing project within the Network University Medicine, a cross-sectional survey was conducted to assess the situation of IC IT systems in 36 university hospitals in Germany. Results Among the most prominent findings were the lack of standardization of IC IT systems and the predominant use of commercial IC IT systems, while the vast majority of hospitals reported inadequacies in the features their IC IT systems provide for their daily work. However, as the pandemic has shown that there is a need for systems that can help improve health care, several German university hospitals have already started this upgrade independently. Conclusions The deep challenges faced by the German health care sector regarding the integration and interoperability of IT systems designed for IC and surveillance are unlikely to be solved through punctual interventions and require collaboration between educational, medical, and administrative institutions.

Background As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies. Objectives The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models. Methods All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source. Results Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%. Conclusion An efficient and portable tool capable of discovering inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.

Background Multisite research networks such as the project “Collaboration on Rare Diseases” connect various hospitals to obtain sufficient data for clinical research. However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems. High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such distributed data. Objectives The aim of this work is the development of an interoperable methodology for assessing the quality of data recorded in heterogeneous sources to improve the quality of rare disease (RD) documentation and support clinical research. Methods We first developed a conceptual framework for DQ assessment. Using this theoretical guidance, we implemented a software framework that provides appropriate tools for calculating DQ metrics and for generating local as well as cross-institutional reports. We further applied our methodology on synthetic data distributed across multiple hospitals using Personal Health Train. Finally, we used precision and recall as metrics to validate our implementation. Results Four DQ dimensions were defined and represented as disjunct ontological categories. Based on these top dimensions, 9 DQ concepts, 10 DQ indicators, and 25 DQ parameters were developed and applied to different data sets. Randomly introduced DQ issues were all identified and reported automatically. The generated reports show the resulting DQ indicators and detected DQ issues. Conclusion We have shown that our approach yields promising results, which can be used for local and cross-institutional DQ assessments. The developed frameworks provide useful methods for interoperable and privacy-preserving assessments of DQ that meet the specified requirements. This study has demonstrated that our methodology is capable of detecting DQ issues such as ambiguity or implausibility of coded diagnoses. It can be used for DQ benchmarking to improve the quality of RD documentation and to support clinical research on distributed data.

Continuous non-invasive blood pressure measurements bear a high potential. Particular in Somnology they allow to derive comfortably the systolic and diastolic blood pressure from an electrocardiogram and a synchronous photoplethysmogram without sleep disruption. In this short article some possible problems of this method are discussed along overnight recordings with a SOMNOtouch NIBP device.

Despite their remarkable performance, deep neural networks remain unadopted in clinical practice, which is considered to be partially due to their lack in explainability. In this work, we apply attribution methods to a pre-trained deep neural network (DNN) for 12-lead electrocardiography classification to open this "black box" and understand the relationship between model prediction and learned features. We classify data from a public data set and the attribution methods assign a "relevance score" to each sample of the classified signals. This allows analyzing what the network learned during training, for which we propose quantitative methods: average relevance scores over a) classes, b) leads, and c) average beats. The analyses of relevance scores for atrial fibrillation (AF) and left bundle branch block (LBBB) compared to healthy controls show that their mean values a) increase with higher classification probability and correspond to false classifications when around zero, and b) correspond to clinical recommendations regarding which lead to consider. Furthermore, c) visible P-waves and concordant T-waves result in clearly negative relevance scores in AF and LBBB classification, respectively. In summary, our analysis suggests that the DNN learned features similar to cardiology textbook knowledge., Accepted version including new experiments on additional database

Data quality in health research encompasses a broad range of aspects and indicators. While some indicators are generic and can be calculated without domain knowledge, others require information about a specific data element. Even more complex are indicators addressing contradictions, that stem from implausible combinations of multiple data elements. In this paper, we investigate how contradictions within interdependent categorical data can be identified and if they give additional information about possible quality issues, their cause, and mitigation options. The 19 data elements that represent four biosample types including their pre-analytic states within the DZHK Biobanking basic set are exported to the CDISC Operational Data Model (ODM), transformed and loaded into a tranSMART instance. Through the implementation of a data quality assessment workflow as a SmartR plug-in, statistical information about the domain-specific consistency of interdependent values are retrieved, assessed, and visualized. Data quality indicators have been selected for the assessment according to common recommendations found in the literature. Different contradictions could be discovered in the dataset including mismatch of interdependent values in the pre-analytic states of blood and urine samples, as well as primary and aliquoted samples. The overall assessment rating shows that 99.61% of the interdependent values are free of contradictions. However, measures within the EDC design to avoid contradictions may result in overestimated missing rates in automatic, item-based quality assessment checks. Through consistency checks on interdependent categorical features, we demonstrated that consistency flaws can be found in the categorical data of biobanking metadata and that they can help to detect issues in the data entry process. Our approach underscores the importance of domain knowledge in the definition of the consistency rules but also knowledge about the EDC implementation of such consistency rules to consider the impact on item-based quality indicators.

The complexity of emergency cases and the number of emergency patients have increased dramatically. Due to a reduced or even missing specialist medical staff in the emergency departments (EDs), medical knowledge is often used without professional supervision for the diagnosis. The result is a failure in diagnosis and treatment, even death in the worst case. Secondary: high expenditure of time and high costs. Using accurate patient data from the German national registry of the medical emergency departments (AKTIN-registry, Home – Notaufnahmeregister (aktin.org)), the most 20 frequent diagnoses were selected for creating explainable artificial intelligence (XAI) models as part of the ENSURE project (ENSURE (umg.eu)). 137.152 samples and 51 features (vital signs and symptoms) were analyzed. The XAI models achieved a mean area under the curve (AUC) one-vs-rest of 0.98 for logistic regression (LR) and 0.99 for the random forest (RF), and predictive accuracies of 0.927 (LR) and 0.99 (RF). Based on its grade of explainability and performance, the best model will be incorporated into a portable CDSS to improve diagnoses and outcomes of ED treatment and reduce cost. The CDSS will be tested in a clinical pilot study at EDs of selected hospitals in Germany.

Many clinical decision support systems (CDSS) have shown good performance in the research context, but only a few have been brought into routine care. Analysis of success or failure factors in the design of CDSS may support translation from development to routine care by guiding CDSS design and development along these factors. In this work, we propose a schema to describe CDSS designs in a consistent way. We focus on design criteria with the aim to investigate the observed translation gap in CDSS. Existing description models on different aspects relevant for CDSS are combined to a comprehensive schema that allows description and comparison of CDSS without limitation to the domain or architecture.

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.

In clinical scenarios, there is an increasing interest in complex computational experiments, as for example the training of Deep Learning models. Reproducibility is an essential property of such experiments, especially if the result contributes to a patient’s treatment. This paper introduces Curious Containers, a software framework for computational reproducibility that treats data, software and runtime environment as decentralized network resources. All experiment resources are described in a single file, using a new format that is compatible with a subset of the Common Workflow Language. Docker is used to deploy the experiment software in a container image, including arbitrary data transmission programs to connect with existing storage solutions. The framework supports Deep Learning applications, that have a high demand in storage and processing capabilities. Large datasets can be mounted inside containers via network filesystems like SSHFS based on the filesystem in user-space technology. The Nvidia-Container-Toolkit enables GPU usage. Curious Containers has been tested in two biomedical scenarios. The first use case is a Deep Learning application for tumor classification in images that requires a large dataset and a GPU. In this context, a prototypical integration of the framework with the existing Data Version Control system for exploratory Deep Learning modeling has been developed. The second use case extends an existing container image, including a scientific workflow for detection and comparison of human protein in mass spectrography data. The container image was originally developed for an archiving platform and could be extended to be compatible with both Curious Containers and cwltool, the Common Workflow Language reference implementation. The presented solution allows for consistent description and execution of computational experiments, while trying to be both flexible and interoperable with existing software and standards. Support for Deep Learning experiments is gaining importance as such systems are increasingly validated as medical decision support systems.

The German government initiated the Network University Medicine (NUM) in early 2020 to improve national research activities on the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. To this end, 36 German Academic Medical Centers started to collaborate on 13 projects, with the largest being the National Pandemic Cohort Network (NAPKON). The NAPKON’s goal is creating the most comprehensive Coronavirus Disease 2019 (COVID-19) cohort in Germany. Within NAPKON, adult and pediatric patients are observed in three complementary cohort platforms (Cross-Sectoral, High-Resolution and Population-Based) from the initial infection until up to three years of follow-up. Study procedures comprise comprehensive clinical and imaging diagnostics, quality-of-life assessment, patient-reported outcomes and biosampling. The three cohort platforms build on four infrastructure core units (Interaction, Biosampling, Epidemiology, and Integration) and collaborations with NUM projects. Key components of the data capture, regulatory, and data privacy are based on the German Centre for Cardiovascular Research. By December 01, 2021, 34 university and 34 non-university hospitals have enrolled 4,241 patients with local data quality reviews performed on 2,812 (66%). 47% were female, the median age was 53 (IQR: 38-63)) and 3 pediatric cases were included. 30% of patients were hospitalized, 11% admitted to an intensive care unit, and 4% of patients deceased while enrolled. 7,143 visits with biosampling in 3,595 patients were conducted by November 29, 2021. In this overview article, we summarize NAPKON’s design, relevant milestones including first study population characteristics, and outline the potential of NAPKON for German and international research activities.Trial registration:· https://clinicaltrials.gov/ct2/show/NCT04768998· https://clinicaltrials.gov/ct2/show/NCT04747366· https://clinicaltrials.gov/ct2/show/NCT04679584

The German government initiated the Network University Medicine (NUM) in early 2020 to improve national research activities on the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. To this end, 36 German Academic Medical Centers started to collaborate on 13 projects, with the largest being the National Pandemic Cohort Network (NAPKON). The NAPKON’s goal is creating the most comprehensive Coronavirus Disease 2019 (COVID-19) cohort in Germany. Within NAPKON, adult and pediatric patients are observed in three complementary cohort platforms (Cross-Sectoral, High-Resolution and Population-Based) from the initial infection until up to three years of follow-up. Study procedures comprise comprehensive clinical and imaging diagnostics, quality-of-life assessment, patient-reported outcomes and biosampling. The three cohort platforms build on four infrastructure core units (Interaction, Biosampling, Epidemiology, and Integration) and collaborations with NUM projects. Key components of the data capture, regulatory, and data privacy are based on the German Centre for Cardiovascular Research. By April 01, 2022, 34 university and 40 non-university hospitals have enrolled 5298 patients with local data quality reviews performed on 4727 (89%). 47% were female, the median age was 52 (IQR 36–62-) and 50 pediatric cases were included. 44% of patients were hospitalized, 15% admitted to an intensive care unit, and 12% of patients deceased while enrolled. 8845 visits with biosampling in 4349 patients were conducted by April 03, 2022. In this overview article, we summarize NAPKON’s design, relevant milestones including first study population characteristics, and outline the potential of NAPKON for German and international research activities.Trial registrationhttps://clinicaltrials.gov/ct2/show/NCT04768998.https://clinicaltrials.gov/ct2/show/NCT04747366.https://clinicaltrials.gov/ct2/show/NCT04679584

The development of clinical decision support systems (CDSS) is complex and requires user-centered planning of assistive interventions. Especially in the setting of emergency care requiring time-critical decisions and interventions, it is important to adapt a CDSS to the needs of the user in terms of acceptance, usability and utility. In the so-called ENSURE project, a user-centered approach was applied to develop the CDSS intervention. In the context of this paper, we present a path to the first mockup development for a CDSS interface by addressing Campbell’s Five Rights within the CDSS workflow.

Introductions: Therapy switches in patients with multiple sclerosis (MS) receiving treatment with fingolimod occur frequently in clinical practice but are not well represented in real-world data. The aim of this study was to identify and characterize treatment switches and reveal sociodemographic/clinical changes over time in fingolimod-treated people with MS (PwMS). Methods: Data on 2536 fingolimod-treated PwMS extracted from the German MS Registry during different time periods were analyzed (2010–2019). Results: Overall, 28.3% of PwMS were treatment-naïve before fingolimod initiation. Interferon beta (30.7%) was the most common pre-fingolimod treatment. Ocrelizumab (19.8%) was the most frequent subsequent treatment in the 944 patients on fingolimod who switched. Between 2010 and 2019, median disease duration at fingolimod initiation decreased from 8.5 to 7.1 years (p < 0.001), and patients taking fingolimod for ≥ 1 year after treatment initiation decreased from 89.6 to 80.5% (p < 0.001). Females (p < 0.001) and young patients (p = 0.003) showed a shorter time on fingolimod. The most frequent reason for switching was disease activity (relapse/MRI) despite treatment. The annualized relapse rate increased from 0.37 in patients on fingolimod to 0.47 after treatment cessation, decreasing to 0.19 after treatment with a subsequent disease-modifying drug (DMD) was initiated. Conclusion: Treatment switches from fingolimod to subsequent DMDs currently occur after shorter treatment durations than 10 years ago, possibly due to the growing treatment spectrum. Planning adequate washout periods is essential and should be done on an individualized basis. CA extern

Automatic electrocardiogram (ECG) analysis has been one of the very early use cases for computer assisted diagnosis (CAD). Most ECG devices provide some level of automatic ECG analysis. In the recent years, Deep Learning (DL) is increasingly used for this task, with the first models that claim to perform better than human physicians. In this manuscript, a pilot study is conducted to evaluate the added value of such a DL model to existing built-in analysis with respect to clinical relevance. 29 12-lead ECGs have been analyzed with a published DL model and results are compared to build-in analysis and clinical diagnosis. We could not reproduce the results of the test data exactly, presumably due to a different runtime environment. However, the errors were in the order of rounding errors and did not affect the final classification. The excellent performance in detection of left bundle branch block and atrial fibrillation that was reported in the publication could be reproduced. The DL method and the built-in method performed similarly good for the chosen cases regarding clinical relevance. While benefit of the DL method for research can be attested and usage in training can be envisioned, evaluation of added value in clinical practice would require a more comprehensive study with further and more complex cases.

Computational methods are nowadays ubiquitous in the field of bioinformatics and biomedicine. Besides established fields like molecular dynamics, genomics or neuroimaging, new emerging methods rely heavily on large scale computational resources. These new methods need to manage Tbytes or Pbytes of data with large-scale structural and functional relationships, TFlops or PFlops of computing power for simulating highly complex models, or many-task processes and workflows for processing and analyzing data. Today, many areas in Life Sciences are facing these challenges. This special issue contains papers showing existing solutions and latest developments in Life Sciences and Computing Sciences to collaboratively explore new ideas and approaches to successfully apply distributed IT-systems in translational research, clinical intervention, and decision-making.

The COVID-19 pandemic has brought along a massive increase in app development. However, most of these apps are not using interoperable data. The COMPASS project of the German COVID-19 Research Network of University Medicine (“Netzwerk Universitätsmedizin (NUM)”) tackles this issue, by offering open-source technology, best practice catalogues, and suggestions for designing interoperable pandemic health applications (https://www.netzwerk-universitaetsmedizin.de/projekte/compass). Therefore, COMPASS conceived a framework that includes automated conformity checks as well as reference implementations for more efficient and pandemic-tailored app developments. It further aims to motivate and support developers to use interoperable standards.

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures., publishedVersion

The Multiple Sclerosis Data Alliance (MSDA), a global multi-stakeholder collaboration, is working to accelerate research insights for innovative care and treatment for people with multiple sclerosis (MS) through better use of real-world data (RWD). Despite the increasing reliance on RWD, challenges and limitations complicate the generation, collection, and use of these data. MSDA aims to tackle sociological and technical challenges arising with scaling up RWD, specifically focused on MS data. MSDA envisions a patient-centred data ecosystem in which all stakeholders contribute and use big data to co-create the innovations needed to advance timely treatment and care of people with MS. We would like to thank the sponsors of the MS Data Alliance. The MSDA receives income from a range of corporate sponsors, recently including: Biogen, BristolMyersSquibb (formerly Celgene), Canopy Growth Corporation, Genzyme, Icometrix, Janssen, Merck, Mylan, Novartis, QMENTA, Quanterix, Roche. We would like to thank the European MS Platform and the University MS Center of Hasselt University for founding the initiative. Next to this, we would like to thank the other core partners for their continued support: the European Charcot Foundation, The European Institute for Innovation through Health Data, the University of Gottingen and KULeuven. A special thanks to our pioneer registries: the UK MS registry and the German MS registry. Peeters, LM (corresponding author), Agoralaan C Bis, B-3590 Diepenbeek, Belgium. Liesbet.peeters@uhasselt.be

The increasingly digitized healthcare system requires new skills from all those involved. In order to impart these competencies, appropriate courses must be developed at educational institutions. In view of the rapid development of new aspects of digitization, this presents a challenge; suitable teaching formats must be developed successively. The establishment of cross-location teaching networks is one way to better meet training needs and to make the necessary spectrum of educational content available. As part of the Medical Informatics Initiative, the HiGHmed consortium is establishing such a teaching network, in the field of medical informatics, which covers many topics related to the digitization of the health care system. Various problem areas in the German education system were identified that hinder the development of the teaching network. These problem areas were prioritized firstly according to the urgency of the solution from the point of view of the HiGHmed consortium and secondly according to existing competencies in the participating societies. A workshop on the four most relevant topics was organized with experts from the German Society for Medical Informatics, Biometry and Epidemiology (GMDS), the Society for Medical Education (GMA) and the HiGHmed consortium. These are: recognition of exam results from teaching modules that are offered digitally and across locations, and their integration into existing curricula; recognition of digital, cross-location teaching in the teachers' teaching load; nationwide uniform competencies for teachers, in order to be able to conduct digital teaching effectively and with comparable quality; technical infrastructure to efficiently and securely communicate and manage the recognition of exam results between educational institutions. For all subject areas, existing preliminary work was identified on the basis of working questions, and short- and long-term needs for action were formulated. Finally, a need for the redesign of a technologically supported syntactic and semantic interoperability of learning performance recording was identified., Das zunehmend digitalisierte Gesundheitswesen erfordert neue Kompetenzen von allen Beteiligten. Um diese Kompetenzen vermitteln zu können, müssen an den Bildungsinstitutionen entsprechende Lehrveranstaltungen entwickelt werden. Dies ist angesichts der rasanten Entwicklung neuer Digitalisierungsaspekte eine Herausforderung; passende Lehrformate müssen sukzessive aufgebaut werden. Die Etablierung standortübergreifender Lehrverbünde ist eine Möglichkeit, den Ausbildungsbedarf besser zu decken und die notwendige Bandbreite an Bildungsinhalten verfügbar zu machen. Im Rahmen der Medizininformatik-Initiative wird im HiGHmed-Konsortium ein solcher Lehrverbund im Fachgebiet der Medizinischen Informatik aufgebaut, in welches viele Themen der Digitalisierung des Gesundheitswesens fallen. Dabei wurden verschiedene Problemfelder im deutschen Bildungswesen identifiziert, welche den Aufbau des Lehrverbundes hemmen. Diese Problemfelder wurden unter Beachtung der Dringlichkeit der Lösung aus Sicht des HiGHmeducation Konsortiums und andererseits nach vorhandenen Kompetenzen in den teilnehmenden Fachgesellschaften priorisiert. Zu den vier relevantesten Themen wurde ein Workshop mit Fachleuten der deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), der Gesellschaft für Medizinische Ausbildung (GMA) und des HiGHmed Konsortiums ausgerichtet. Diese sind: Anerkennung von Prüfungsleistungen aus Lehrmodulen, welche digital und standortübergreifend angeboten werden, und Integration in bestehende Curricula; Anerkennung digitaler, standortübergreifender Lehre im Deputat der Lehrenden; bundesweit einheitliche Kompetenzen für Lehrende, um digitale Lehre effektiv und mit vergleichbarer Qualität betreiben zu können; technische Infrastrukturen, um die Anerkennung zwischen Bildungseinrichtungen effizient und sicher kommunizieren und verwalten zu können. Für alle Themengebiete wurden anhand von Arbeitsfragen existierende Vorarbeiten identifiziert sowie kurz- und langfristige Handlungsbedarfe formuliert. Abschließend wurde themenübergreifend ein Bedarf für die Neugestaltung einer technologisch unterstützten syntaktischen und semantischen Interoperabilität der Erfassung von Lernleistungen festgestellt., GMS Journal for Medical Education; 37(6):Doc56

Sleep is a fundamental biological process crucial for survival and health of (not only) humans. But many circumstances like physiological and mental disorders, environment and lifestyle may affect healthy sleep. To date, 88 different sleep disorders are internationally recognized. They cover a broad field of medical areas. Analysis of human sleep is typically based on multidimensional biosignal recordings, so called polysomnographies (PSG). Therefore research often includes digital signal processing. Clinical sleep research is an inherent multidisciplinary field. Inter-institutional and interdisciplinary collaborations are required to address the complexity of sleep regulation and disturbance. But to date, collaborative sleep research is poorly supported by IT systems. In particular, the management and processing of PSGs is challenging. A large variety of PSG devices, data formats, measurement procedures and quality variations impedes consistent biosignal data processing. In this manuscript we introduce a virtual research platform supporting inter-institutional data sharing and processing. The infrastructure is based on XNAT—a free and open source neuroimaging research platform, a loosely coupled service oriented architecture and scalable virtualization in the back end. The system is capable of local pseudonymization of biosignal data, mapping to a standardized set of parameters and automatic quality assessment. Terms and quality measures are derived from the “Manual for the Scoring of Sleep and Associated Events” of the American Academy of Sleep Medicine (AASM), the de facto standard for diagnostic biosignal analysis in sleep medicine.

This paper describes the teaching and learning experiences made at the women's Bachelor degree programme ‘Computer science and business administration’. It focuses on the consistent orientation towards practical experiences. Different courses are described showing the strong emphasis on practical training. One special course is described in more detail: ‘Industrial project’, which is held in both semesters, third and fifth, and where teams of students perform real projects for private clients from industry. It is shown that the entire programme is geared to offer practical experiences as much as possible to the students. After two semesters of teaching foundations of computer science, the students carry out the first industrial project supported jointly with advanced students from the fifth semester. At the fourth semester the students complete their 4-5 months internship, the fifth semester students attend the industrial project again and in the sixth semester the students write their Bachelor thesis, usually at an industrial partner's site. The women-only Bachelor degree programme has started in 2009 and has been developed to a success story since then. Almost all graduates have found a job immediately after the studies, or have continued a subsequent Master programme.

Introduction We investigate how characteristics of sleep-wake dynamics in humans are modified by narcolepsy, a clinical condition that is supposed to destabilize sleep-wake regulation. Subjects with and without cataplexy are considered separately. Differences in sleep scoring habits as a possible confounder have been examined. Aims and methods Four groups of subjects are considered: narcolepsy patients from China with (n = 88) and without (n = 15) cataplexy, healthy controls from China (n = 110) and from Europe (n = 187, 2 nights each). After sleep-stage scoring and calculation of sleep characteristic parameters, the distributions of wake-episode durations and sleep-episode durations are determined for each group and fitted by power laws (exponent α) and by exponentials (decay time τ). Results We find that wake duration distributions are consistent with power laws for healthy subjects (China: α = 0.88, Europe: α = 1.02). Wake durations in all groups of narcolepsy patients, however, follow the exponential law (τ = 6.2-8.1 min). All sleep duration distributions are best fitted by exponentials on long time scales (τ = 34-82 min). Conclusions We conclude that narcolepsy mainly alters the control of wake-episode durations but not sleep-episode durations, irrespective of cataplexy. Observed distributions of shortest wake and sleep durations suggest that differences in scoring habits regarding the scoring of short-term sleep stages may notably influence the fitting parameters but do not affect the main conclusion.

Vibration synchronized magnetic resonance imaging of harmonically oscillating tissue interfaces is proposed for cardiac magnetic resonance elastography. The new approach exploits cardiac triggered cine imaging synchronized with extrinsic harmonic stimulation (f = 22.83 Hz) to display oscillatory tissue deformations in magnitude images. Oscillations are analyzed by intensity threshold-based image processing to track wave amplitude variations over the cardiac cycle. In agreement to literature data, results in 10 volunteers showed that endocardial wave amplitudes during systole (0.13 ± 0.07 mm) were significantly lower than during diastole (0.34 ± 0.14 mm, P < 0.001). Wave amplitudes were found to decrease 117 ± 40 ms before myocardial contraction and to increase 75 ± 31 ms before myocardial relaxation. Vibration synchronized magnetic resonance imaging improves the temporal resolution of magnetic resonance elastography as it overcomes the use of extra motion encoding gradients, is less sensitive to susceptibility artifacts, and does not suffer from dynamic range constraints frequently encountered in phase-based magnetic resonance elastography.

In this paper, we describe the grid integration of medical image processing applications as grid workflows. The workflow management system is able to execute all tasks related to grid communication, such as authorization, scheduling and monitoring. It remains to the developer to make the code accessible for the workflow manager, and to define, what to do with it. Coarse-grained parallelization of processing steps for runtime reduction can easily be realized. We describe the procedure how to port the code to the grid and show exemplarily the integration of segmentation and registration algorithms for transrectal ultrasound guided prostate biopsies.

A microbubble in a sound wave oscillates in volume and translates unsteadily. The two motions are coupled. In large-scale simulations of the structure of bubble clouds driven by acoustic fields, it has been of significant convenience to decouple volume oscillations and translation, as an approximation. The errors of this decoupling approximation were considered in an earlier presentation [A. J. Reddy and A. J. Szeri, J. Acoust. Soc. Am. 112, 1346–1352 (2002)], in the parameter range of interest in medical ultrasound. In this work, the approximation is reexamined for a much broader range of driving frequencies and bubble sizes. Solving the equation of motion for linearly oscillating bubbles, it is found that even for weak acoustic forcing, the translation speed obtained with the decoupling approximation can be in error as much as 30% relative to the translation speed in the full equations. The error depends on the bubble size, the driving frequency, and the liquid properties. The results are presented in a...

Filamentary formations of acoustic cavitation bubbles in an ultrasonic resonator are recorded by high-speed stereoscopic means. The bubble locations and motions are reconstructed in three dimensions, and a velocity distribution of bubbles is obtained. Experimental bubble trajectories are compared to a one-to-one simulation by a particle modeling approach which shows reasonable agreement. Such investigations are important for a better understanding of the mechanisms taking place in applications of intense ultrasound in liquids, and for verification and improvement of particle modeling of cavitation bubbles.

Sonoluminescing single bubbles driven simultaneously by two harmonic frequencies were recently reported to increase the maximum light output up to a factor of 3 with respect to single mode excitation. In this paper, experimental and numerical results on single-bubble sonoluminescence (SBSL) in an air/water system using the fundamental mode of 25 kHz and the second harmonic at 50 kHz are presented. The region of light emission is mapped in the three-dimensional parameter space spanned by the two driving pressure amplitudes and their relative phase. Good agreement was seen between measured light output, maximum bubble radius, and stability boundaries and the numerical model which is based on spherical bubble oscillations regarding diffusive and shape stability. The maximum brightness was enhanced by a factor up to 2.5 with respect to single mode SBSL. However, long-term measurements reveal great variation of the emission at fundamental mode driven SBSL and of the boost factor reached with two frequencies. The overall brightness maxima of both excitation methods within a period of several hours turn out to show little difference.

Neurosciencific research is increasingly based on image analysis methods. Large sets of imaging data are processed using complex image analysis tools. While today magnetic resonance imaging (MRI) is widely used for both functional and anatomical analysis of the human brain, new imaging modalities are beginning to prove their capabilities for neurological research. Among them, optical coherence tomography (OCT) allows for noninvasive visualization of anatomical structures on a micrometer scale. Becoming a standard diagnostic tool in ophthalmology, it is of rising interest for neurological research. Crucial to all data analysis methods is the quality of the input data. The platform presented in this paper is designed for automatic quality assessment of retinal OCTs. It extends the image management platform XNAT by services to calculate and store quality measures. It is also extensible regarding new quality measure algorithms, allowing the developer to upload Matlab code, compile it for the infrastructure's hardware architecture and test it in the system. The image processing tools to calculate the quality measures are provided as a cloud-based service employing OpenStack as underlying IT infrastructure. The prototype implementation encompassing security and performance aspects are presented.

Neuroimaging is a field that benefits from distributed computing infrastructures (DCIs) to perform data processing and analysis, which is often achieved using Grid workflow systems. Collaborative research in neuroimaging requires ways to facilitate exchange between different groups, in particular to enable sharing, re-use and interoperability of applications implemented as workflows. The SHIWA project provides solutions to facilitate sharing and exchange of workflows between workflow systems and DCI resources. In this paper we present and analyse how the SHIWA Platform was used to implement various cases in which workflow exchange supports collaboration in neuroscience. The SHIWA Platform and the implemented solutions are described and analysed from a "user" perspective, in this case workflow developers and neuroscientists. We conclude that the platform in its current form is valuable for these cases, and we identify remaining challenges.