Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV.

The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.

[Interoperability Working Group: core dataset and information systems for data integration and data exchange in the Medical Informatics Initiative]. / Arbeitsgruppe Interoperabilität: Kerndatensatz und Informationssysteme für Integration und Austausch von Daten in der Medizininformatik-Initiative.

The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.

[Development of competencies in the Medical Informatics Initiative (MII)-educational offers for a competent and secure handling of medical data]. / Kompetenzentwicklung in der Medizininformatik-Initiative (MII) ­ Lehrangebote für einen souveränen und sicheren Umgang mit medizinischen Daten.

In order to achieve the goals of the Medical Informatics Initiative (MII), staff with skills in the field of medical informatics and data science are required. Each consortium has established training activities. Further, cross-consortium activities have emerged. This article describes the concepts, implemented programs, and experiences in the consortia. Fifty-one new professorships have been established and 10 new study programs have been created: 1 bachelor's degree and 6 consecutive and 3 part-time master's degree programs. Further, learning and training opportunities can be used by all MII partners. Certification and recognition opportunities have been created.The educational offers are aimed at target groups with a background in computer science, medicine, nursing, bioinformatics, biology, natural science, and data science. Additional qualifications for physicians in computer science and computer scientists in medicine seem to be particularly important. They can lead to higher quality in software development and better support for treatment processes by application systems.Digital learning methods were important in all consortia. They offer flexibility for cross-location and interprofessional training. This enables learning at an individual pace and an exchange between professional groups.The success of the MII depends largely on society's acceptance of the multiple use of medical data in both healthcare and research. The information required for this is provided by the MII's public relations work. There is also an enormous need in society for medical and digital literacy.

[Foundations for the scientific use of extensive health care data in Germany-results of the Data Sharing working group of the Medical Informatics Initiative]. / Grundlagen für die wissenschaftliche Nutzung umfangreicher Versorgungsdaten in Deutschland ­ Ergebnisse der AG Data Sharing der Medizininformatik-Initiative.

Healthcare data are an important resource in applied medical research. They are available multicentrically. However, it remains a challenge to enable standardized data exchange processes between federal states and their individual laws and regulations. The Medical Informatics Initiative (MII) was founded in 2016 to implement processes that enable cross-clinic access to healthcare data in Germany. Several working groups (WGs) have been set up to coordinate standardized data structures (WG Interoperability), patient information and declarations of consent (WG Consent), and regulations on data exchange (WG Data Sharing). Here we present the most important results of the Data Sharing working group, which include agreed terms of use, legal regulations, and data access processes. They are already being implemented by the established Data Integration Centers (DIZ) and Use and Access Committees (UACs). We describe the services that are necessary to provide researchers with standardized data access. They are implemented with the Research Data Portal for Health, among others. Since the pilot phase, the processes of 385 active researchers have been used on this basis, which, as of April 2024, has resulted in 19 registered projects and 31 submitted research applications.

Glucose tolerance and insulin resistance/sensitivity associate with retinal layer characteristics: the LIFE-Adult-Study.

AIMS/HYPOTHESIS: As the prevalence of insulin resistance and glucose intolerance is increasing throughout the world, diabetes-induced eye diseases are a global health burden. We aim to identify distinct optical bands which are closely related to insulin and glucose metabolism, using non-invasive, high-resolution spectral domain optical coherence tomography (SD-OCT) in a large, population-based dataset. METHODS: The LIFE-Adult-Study randomly selected 10,000 participants from the population registry of Leipzig, Germany. Cross-sectional, standardised phenotyping included the assessment of various metabolic risk markers and ocular imaging, such as SD-OCT-derived thicknesses of ten optical bands of the retina. Global and Early Treatment Diabetic Retinopathy Study (ETDRS) subfield-specific optical retinal layer thicknesses were investigated in 7384 healthy eyes of 7384 participants from the LIFE-Adult-Study stratified by normal glucose tolerance, prediabetes (impaired fasting glucose and/or impaired glucose tolerance and/or HbA1c 5.7-6.4% [39-47 mmol/mol]) and diabetes. The association of optical retinal band characteristics with different indices of glucose tolerance (e.g. fasting glucose, area under the glucose curve), insulin resistance (e.g. HOMA2-IR, triglyceride glucose index), or insulin sensitivity (e.g. estimated glucose disposal rate [eGDR], Stumvoll metabolic clearance rate) was determined using multivariable linear regression analyses for the individual markers adjusted for age, sex and refraction. Various sensitivity analyses were performed to validate the observed findings. RESULTS: In the study cohort, nine out of ten optical bands of the retina showed significant sex- and glucose tolerance-dependent differences in band thicknesses. Multivariable linear regression analyses revealed a significant, independent, and inverse association between markers of glucose intolerance and insulin resistance (e.g. HOMA2-IR) with the thickness of the optical bands representing the anatomical retinal outer nuclear layer (ONL, standardised ß=-0.096; p<0.001 for HOMA2-IR) and myoid zone (MZ; ß=-0.096; p<0.001 for HOMA2-IR) of the photoreceptors. Conversely, markers of insulin sensitivity (e.g. eGDR) positively and independently associated with ONL (ß=0.090; p<0.001 for eGDR) and MZ (ß=0.133; p<0.001 for eGDR) band thicknesses. These global associations were confirmed in ETDRS subfield-specific analyses. Sensitivity analyses further validated our findings when physical activity, neuroanatomical cell/tissue types and ETDRS subfield categories were investigated after stratifying the cohort by glucose homeostasis. CONCLUSIONS/INTERPRETATION: An impaired glucose homeostasis associates with a thinning of the optical bands of retinal ONL and photoreceptor MZ. Changes in ONL and MZ thicknesses might predict early metabolic retinal alterations in diabetes.

The different areas of chronic stress and food addiction: Results from the LIFE-Adult-Study.

There is an empirical association between stress and symptoms of food addiction (FA), but it is still not clear which domains of stress are the most relevant when it comes to FA, limiting the ability of researchers and practitioners to address problematic eating-related health outcomes. In order to address this gap in the literature, we analysed how different domains of chronic stress are related to FA. We used data from a subsample of the LIFE-Adult-Study (N = 1172), a German cohort study. We conducted a linear regression analysis with stress domains (Trier Inventory for Chronic Stress, TICS) as predictors of FA (Yale Food Addiction Scale, YFAS). In the second regression analysis we included sociodemographic variables, personality, and smoking as control variables. There was a significant and positive association between Social Overload, Work Discontent, Excessive Demands from Work, and Chronic Worrying and FA. After adding control variables, only Social Overload, Excessive Demands from Work, and Chronic Worrying remained significant predictors. Connections between stress domains and FA can serve as starting points for the development of meaningful interventions that support individuals self-care strategies (Social Overload), complexity management (Excessive Demands from Work), and coping with negative emotions (Chronic Worrying).

Automated detection of genetic relatedness from fundus photographs using Siamese Neural Networks.

Heritability of common eye diseases and ocular traits are relatively high. Here, we develop an automated algorithm to detect genetic relatedness from color fundus photographs (FPs). We estimated the degree of shared ancestry amongst individuals in the UK Biobank using KING software. A convolutional Siamese neural network-based algorithm was trained to output a measure of genetic relatedness using 7224 pairs (3612 related and 3612 unrelated) of FPs. The model achieved high performance for prediction of genetic relatedness; when computed Euclidean distances were used to determine probability of relatedness, the area under the receiver operating characteristic curve (AUROC) for identifying related FPs reached 0.926. We performed external validation of our model using FPs from the LIFE-Adult study and achieved an AUROC of 0.69. An occlusion map indicates that the optic nerve and its surrounding area may be the most predictive of genetic relatedness. We demonstrate that genetic relatedness can be captured from FP features. This approach may be used to uncover novel biomarkers for common ocular diseases.

Perceived stress of mental demands at work, objective stress and resilience - an analysis of the LIFE-Adult-study.

BACKGROUND: So far, previous research suggests positive effects of mental demands at the workplace. However, it may depend on how stressfull these demands are perceived on an individual level. OBJECTIVE: The aim was to build on previous research by investigating how mental demands are related to stress, overload, and work discontent and whether this relationship is mediated by individuals resources, such as resilience. METHOD: A sub-sample of the LIFE Adult Cohort (n = 480) was asked to answer questions on sociodemographic characteristics, objective stress (using the Trier Inventory of Chronic Stress (TICS)), and perceptions of stress with regard to verbal and executive mental demands at work. RESULTS: According to generalized linear regression models, higher verbal as well as executive mental demands were associated with higher levels of chronic stress, work overload and discontent. Higher levels of resilience were associated with lower levels of these outcomes. Analyses regarding interaction effects revealed that the interaction between resilience and perceived stress of verbal mental demands was significant only in terms of work overload. CONCLUSION: Higher perceived stressfulness of mental demands was associated with higher chronic stress, work overload and work discontent. Therefore, mental demands should be targeted by occupational interventions that aim to improve job conditions and employees' overall well-being. Besides resilience, other potential influencers or personal resources should be focused on in future studies to develop interventions.

Association of Systemic Medication Use with Glaucoma and Intraocular Pressure: The European Eye Epidemiology Consortium.

PURPOSE: To investigate the association of commonly used systemic medications with glaucoma and intraocular pressure (IOP) in the European population. DESIGN: Meta-analysis of 11 population-based cohort studies of the European Eye Epidemiology Consortium. PARTICIPANTS: The glaucoma analyses included 143 240 participants and the IOP analyses included 47 177 participants. METHODS: We examined associations of 4 categories of systemic medications-antihypertensive medications (ß-blockers, diuretics, calcium channel blockers [CCBs], α-agonists, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers), lipid-lowering medications, antidepressants, and antidiabetic medications-with glaucoma prevalence and IOP. Glaucoma ascertainment and IOP measurement method were according to individual study protocols. Results of multivariable regression analyses of each study were pooled using random effects meta-analyses. Associations with antidiabetic medications were examined in participants with diabetes only. MAIN OUTCOME MEASURES: Glaucoma prevalence and IOP. RESULTS: In the meta-analyses of our maximally adjusted multivariable models, use of CCBs was associated with a higher prevalence of glaucoma (odds ratio [OR], 1.23; 95% confidence interval [CI], 1.08 to 1.39). This association was stronger for monotherapy of CCBs with direct cardiac effects (OR, 1.96; 95% CI, 1.23 to 3.12). No other antihypertensive medications, lipid-lowering medications, antidepressants, or antidiabetic medications were associated with glaucoma. Use of systemic ß-blockers was associated with a lower IOP (ß coefficient, -0.33 mmHg; 95% CI, -0.57 to -0.08 mmHg). Monotherapy of both selective systemic ß-blockers (ß coefficient, -0.45 mmHg; 95% CI -0.74 to -0.16 mmHg) and nonselective systemic ß-blockers (ß coefficient, -0.54 mmHg; 95% CI, -0.94 to -0.15 mmHg) was associated with lower IOP. A suggestive association was found between use of high-ceiling diuretics and lower IOP (ß coefficient, -0.30 mmHg; 95% CI, -0.47 to -0.14 mmHg) but not when used as monotherapy. No other antihypertensive medications, lipid-lowering medications, antidepressants, or antidiabetic medications were associated with IOP. CONCLUSIONS: We identified a potentially harmful association between use of CCBs and glaucoma prevalence. Additionally, we observed and quantified the association of lower IOP with systemic ß-blocker use. Both findings potentially are important, given that patients with glaucoma frequently use systemic antihypertensive medications. Determining causality of the CCB association should be a research priority. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Feasibility and repeatability of ocular biometry measured with IOLMaster 700 in a large population-based study.

PURPOSE: To evaluate the feasibility and repeatability of IOLMaster 700 biometry measurements in an adult population. Furthermore, to assess the value of the Quality Indicators (QIs) provided by the device. METHOD: As part of the large population-based Leipzig Research Centre for Civilization Diseases (LIFE) Adult-Study, randomly selected participants from Leipzig, Germany were evaluated with the ZEISS IOLMaster 700. Age range was 26-85 years, with 53% of participants above 70 years of age. Axial length (AL), central corneal thickness (CCT), anterior chamber depth (ACD), lens thickness (LT) and keratometry (K) were assessed in 1767 right eyes. Measurements were repeated twice and in a subset of 1331 eyes, three times. Measurement feasibility was evaluated for three levels; successful, with warnings and failed, using the inbuilt QIs. Repeatability was assessed as within-subject standard deviation (SD) and repeatability limits were calculated. RESULTS: First measurement success rate for phakic eyes was over 99% for AL, CCT, ACD, over 98% for LT and over 97% for K. K had 16% eyes with warnings and the recommendation to repeat the measurement. Excluding the measurements with warnings resulted in a reduction of mean SD for AL from 48 to 4 µm and for mean K from 0.08 to 0.04 D. Repeatability for phakic eyes was 8 µm for AL, CCT, ACD and LT and 2.3 µm for CCT; 0.07 D and 0.12 D for mean K and delta K, respectively, for phakic cases without warnings (two measurements). CONCLUSIONS: In our population-based sample, the IOLMaster 700 collected data for AL, CCT, ACD, LT and K from the vast majority of eyes. Considering the built-in QIs improved the measurement variability substantially. Repeatability measurements indicate that clinically meaningful changes can be detected reliably with this instrument.

Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects.

This work presents a novel fully automated method for retinal analysis in images acquired with a flood illuminated adaptive optics retinal camera (AO-FIO). The proposed processing pipeline consists of several steps: First, we register single AO-FIO images in a montage image capturing a larger retinal area. The registration is performed by combination of phase correlation and the scale-invariant feature transform method. A set of 200 AO-FIO images from 10 healthy subjects (10 images from left eye and 10 images from right eye) is processed into 20 montage images and mutually aligned according to the automatically detected fovea center. As a second step, the photoreceptors in the montage images are detected using a method based on regional maxima localization, where the detector parameters were determined with Bayesian optimization according to manually labeled photoreceptors by three evaluators. The detection assessment, based on Dice coefficient, ranges from 0.72 to 0.8. In the next step, the corresponding density maps are generated for each of the montage images. As a final step, representative averaged photoreceptor density maps are created for the left and right eye and thus enabling comprehensive analysis across the montage images and a straightforward comparison with available histological data and other published studies. Our proposed method and software thus enable us to generate AO-based photoreceptor density maps for all measured locations fully automatically, and thus it is suitable for large studies, as those are in pressing need for automated approaches. In addition, the application MATADOR (MATlab ADaptive Optics Retinal Image Analysis) that implements the described pipeline and the dataset with photoreceptor labels are made publicly available.

Association of lipid-lowering drugs and antidiabetic drugs with age-related macular degeneration: a meta-analysis in Europeans.

BACKGROUND/AIMS: To investigate the association of commonly used systemic medications with prevalent age-related macular degeneration (AMD) in the general population. METHODS: We included 38 694 adults from 14 population-based and hospital-based studies from the European Eye Epidemiology consortium. We examined associations between the use of systemic medications and any prevalent AMD as well as any late AMD using multivariable logistic regression modelling per study and pooled results using random effects meta-analysis. RESULTS: Between studies, mean age ranged from 61.5±7.1 to 82.6±3.8 years and prevalence ranged from 12.1% to 64.5% and from 0.5% to 35.5% for any and late AMD, respectively. In the meta-analysis of fully adjusted multivariable models, lipid-lowering drugs (LLD) and antidiabetic drugs were associated with lower prevalent any AMD (OR 0.85, 95% CI=0.79 to 0.91 and OR 0.78, 95% CI=0.66 to 0.91). We found no association with late AMD or with any other medication. CONCLUSION: Our study indicates a potential beneficial effect of LLD and antidiabetic drug use on prevalence of AMD across multiple European cohorts. Our findings support the importance of metabolic processes in the multifactorial aetiology of AMD.

Connecting chronic stress and anxiety: a multi-dimensional perspective.

Studies show a connection between anxiety and stress, but with little differentiation between different domains of stress. In this article, we utilize a multi-dimensional approach to better understand the relationship between different chronic stress domains and anxiety. This will allow researchers to identify and address those areas of stress that are most relevant with regard to anxiety. We used data from a sub sample of the LIFE-Adult-Study (n = 1085) to analyze the association between nine different areas of chronic stress (Trier Inventory for Chronic Stress, TICS) and anxiety (General Anxiety Disorder 7, GAD-7), controlling for sociodemographic variables, personality, and social support. There was a significant and positive association between Work Overload, Pressure to Perform, Social Tensions, Social Isolation, Chronic Worrying, and anxiety. After including the control variables, only Work Overload and Chronic Worrying remained significant. By focusing on Work Overload and Chronic Worrying researchers, practitioners, and policy makers can help to mitigate anxiety and related health problems in the population in an efficient way.

The Leipzig Health Atlas-An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online.

BACKGROUND: Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains. OBJECTIVE: The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects. METHODS: Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups. RESULTS: The LHA platform is in productive mode (https://www.health-atlas.de/). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany.

Different Areas of Chronic Stress and Their Associations with Depression.

BACKGROUND: Research shows a connection between stress and depression, but there is little differentiation between areas of stress, making it difficult to identify and address specific areas in the context of public health measures. We utilized a multi-dimensional approach to chronic stress to better understand the relationship between different areas of stress and depression. METHODS: We conducted linear regression analyses and used data from a sub-sample of the LIFE-Adult-Study (N = 1008) to analyze the connection between nine different areas of chronic stress (TICS) and depression (CES-D). In the second analysis, we controlled for sociodemographic variables, personality, physical activity, and social support. RESULTS: There was a significant positive association between the stress domains Excessive Demands from Work, Lack of Social Recognition, Social Isolation, and Chronic Worrying and depression and a significant negative association between Pressure to Perform and depression. After adding control variables, only Pressure to Perform, Social Isolation, and Chronic Worrying remained significant predictors. CONCLUSIONS: By focusing on the connections between chronic stress and depression, researchers can help identify the areas that matter most and contribute to the creation of meaningful and efficient interventions. On the basis of our results, measures for the prevention of depression that focus on the reduction of worrying and social isolation are recommended.

Anxiety and Food Addiction in Men and Women: Results From the Longitudinal LIFE-Adult-Study.

Background: Anxiety is a widespread phenomenon, and it is connected to disordered eating and obesity. We want to analyze the connection between anxiety and food addiction (FA) over two points in time to better understand the directionality of the association. Since there are gender differences with regard to anxiety and eating, we are also interested in differences between men and women. Methods: We used data from the population-based LIFE-Adult-Study (N = 1,474) at time 1 (baseline) and time 2 (first follow-up) to analyze the connections between anxiety (GAD-7) and FA (YFAS) using a multiple group latent cross-lagged panel model with female and male participants as groups. We controlled for age, marital status, socioeconomic status and social support. Results: Anxiety (women: ß = 0.50, p ≤ 0.001; men: ß = 0.59, p ≤ 0.001) as well as FA (women: ß = 0.37, p ≤ 0.001; men: ß = 0.58, p ≤ 0.001) exhibited stability over time for both genders. We found a significant association between anxiety at time 1 and FA at time 2 for women (ß = 0.25, p ≤ 0.001) but not for men (ß = 0.04, p = 0.10), and significant associations between FA at time 1 and anxiety at time 2 for women (ß = 0.23, p ≤ 0.001) as well as men (ß = 0.21, p ≤ 0.001). Conclusion: Food addiction longitudinally affects anxiety, independent of gender and other sociodemographic variables. In addition, anxiety affects subsequent FA as well, but only in women. Interventions that address FA could reduce anxiety in men and women, while interventions that mitigate anxiety could help prevent FA in women.

A Privacy-Preserving Distributed Analytics Platform for Health Care Data.

BACKGROUND: In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of health care data. However, data protection regulations prohibit data centralisation for analysis purposes because of potential privacy risks like the accidental disclosure of data to third parties. Therefore, alternative data usage policies, which comply with present privacy guidelines, are of particular interest. OBJECTIVE: We aim to enable analyses on sensitive patient data by simultaneously complying with local data protection regulations using an approach called the Personal Health Train (PHT), which is a paradigm that utilises distributed analytics (DA) methods. The main principle of the PHT is that the analytical task is brought to the data provider and the data instances remain in their original location. METHODS: In this work, we present our implementation of the PHT paradigm, which preserves the sovereignty and autonomy of the data providers and operates with a limited number of communication channels. We further conduct a DA use case on data stored in three different and distributed data providers. RESULTS: We show that our infrastructure enables the training of data models based on distributed data sources. CONCLUSION: Our work presents the capabilities of DA infrastructures in the health care sector, which lower the regulatory obstacles of sharing patient data. We further demonstrate its ability to fuel medical science by making distributed data sets available for scientists or health care practitioners.

[Making COVID-19 research data more accessible-building a nationwide information infrastructure]. / COVID-19-Forschungsdaten leichter zugänglich machen ­ Aufbau einer bundesweiten Informationsinfrastruktur.

Public health research and epidemiological and clinical studies are necessary to understand the COVID-19 pandemic and to take appropriate action. Therefore, since early 2020, numerous research projects have also been initiated in Germany. However, due to the large amount of information, it is currently difficult to get an overview of the diverse research activities and their results. Based on the "Federated research data infrastructure for personal health data" (NFDI4Health) initiative, the "COVID-19 task force" is able to create easier access to SARS-CoV-2- and COVID-19-related clinical, epidemiological, and public health research data. Therefore, the so-called FAIR data principles (findable, accessible, interoperable, reusable) are taken into account and should allow an expedited communication of results. The most essential work of the task force includes the generation of a study portal with metadata, selected instruments, other study documents, and study results as well as a search engine for preprint publications. Additional contents include a concept for the linkage between research and routine data, a service for an enhanced practice of image data, and the application of a standardized analysis routine for harmonized quality assessment. This infrastructure, currently being established, will facilitate the findability and handling of German COVID-19 research. The developments initiated in the context of the NFDI4Health COVID-19 task force are reusable for further research topics, as the challenges addressed are generic for the findability of and the handling with research data.

Distributed Skin Lesion Analysis Across Decentralised Data Sources.

Skin cancer has become the most common cancer type. Research has applied image processing and analysis tools to support and improve the diagnose process. Conventional procedures usually centralise data from various data sources to a single location and execute the analysis tasks on central servers. However, centralisation of medical data does not often comply with local data protection regulations due to its sensitive nature and the loss of sovereignty if data providers allow unlimited access to the data. The Personal Health Train (PHT) is a Distributed Analytics (DA) infrastructure bringing the algorithms to the data instead of vice versa. By following this paradigm shift, it proposes a solution for persistent privacy- related challenges. In this work, we present a feasibility study, which demonstrates the capability of the PHT to perform statistical analyses and Machine Learning on skin lesion data distributed among three Germany-wide data providers.

Ontological Modelling and Execution of Phenotypic Queries in the Leipzig Health Atlas.

Sharing data is of great importance for research in medical sciences. It is the basis for reproducibility and reuse of already generated outcomes in new projects and in new contexts. FAIR data principles are the basics for sharing data. The Leipzig Health Atlas (LHA) platform follows these principles and provides data, describing metadata, and models that have been implemented in novel software tools and are available as demonstrators. LHA reuses and extends three different major components that have been previously developed by other projects. The SEEK management platform is the foundation providing a repository for archiving, presenting and secure sharing a wide range of publication results, such as published reports, (bio)medical data as well as interactive models and tools. The LHA Data Portal manages study metadata and data allowing to search for data of interest. Finally, PhenoMan is an ontological framework for phenotype modelling. This paper describes the interrelation of these three components. In particular, we use the PhenoMan to, firstly, model and represent phenotypes within the LHA platform. Then, secondly, the ontological phenotype representation can be used to generate search queries that are executed by the LHA Data Portal. The PhenoMan generates the queries in a novel domain specific query language (SDQL), which is specific for data management systems based on CDISC ODM standard, such as the LHA Data Portal. Our approach was successfully applied to represent phenotypes in the Leipzig Health Atlas with the possibility to execute corresponding queries within the LHA Data Portal.