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959 Publications visible to you, out of a total of 959
Health Informatics learning objectives on an interoperable, collaborative platform
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Catalogues of learning objectives for Biomedical and Health Informatics are relevant prerequisites for systematic and effective qualification. Catalogue management needs to integrate different catalogues … and support collaborative revisioning. The Health Informatics Learning Objectives Navigator (HI-LONa) offers an open, interoperable platform based on Semantic Web Technology. At present HI-LONa contains 983 learning objectives of three relevant catalogues. HI-LONa successfully supported a multiprofessional consensus process.

Authors: Cord Spreckelsen, Ulrike Schemmann, Lo An Phan-Vogtmann, André Scherag, Alfred Winter, Birgit Schneider

Date Published: 1st May 2021

Publication Type: Journal article

Ontological modelling and execution of phenotypic queries in the Leipzig Health Atlas
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

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.

Authors: Alexandr Uciteli, Christoph Beger, Jonas Wagner, Alexander Kiel, Frank A Meineke, Sebastian Stäubert, Matthias Löbe, René Hänsel, Judith Schuster, Toralf Kirsten, Heinrich Herre

Date Published: 1st May 2021

Publication Type: InCollection

Ontological modelling and FHIR Search based representation of basic eligibility criteria
LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Planning clinical studies to check medical hypotheses requires the specification of eligibility criteria in order to identify potential study participants. Electronically available patient data allows … to support the recruitment of patients for studies. The Smart Medical Information Technology for Healthcare (SMITH) consortium aims to establish data integration centres to enable the innovative use of available healthcare data for research and treatment optimization. The data from the electronic health record of patients in the participating hospitals is integrated into a Health Data Storage based on the Fast Healthcare Interoperability Resources standard (FHIR), developed by HL7. In SMITH, FHIR Search is used to query the integrated data. An investigation has shown the advantages and disadvantages of using FHIR Search for specifying eligibility criteria. This paper presents an approach for modelling eligibility criteria as well as for generating and executing FHIR Search queries. Our solution is based on the Phenotype Manager, a general ontological phenotyping framework to model and calculate phenotypes using the Core Ontology of Phenotypes.

Authors: A. Uciteli, C. Beger, J. Wagner, T. Kirsten, F. A. Meineke, S. Staubert, M. Lobe, H. Herre

Date Published: 26th Apr 2021

Publication Type: Journal article

Integration of allergy documentation into an interoperable archiving and communication platform to improve patient care and clinical research at the Jena University Hospital
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

As part of the digitalization strategy for medical documentation, we introduced a vendor-neutral archive (VNA) at the Jena University Hospital. The VNA provides data based on interoperable formats for … all clinical systems and for communication with other actors as well as for secondary data use. HL7 FHIR is used as a standard for structuring individual data. Using the example of allergy documentation, the paper describes objectives, work status, and experiences from several sub-projects currently being pursued in Jena to establish FHIR. Challenges in information modelling, semantic annotation, connection of clinical sub-systems and integration of FHIR resources in archiving structures and processes are discussed and the necessary cooperation for profiling technical standards is indicated.

Authors: Henner M. Kruse, Alexander Helhorn, Lo An Phan-Vogtmann, Julia Palm, Eric Thomas, Andreas Iffland, Andrew Heidel, Susanne Müller, Kutaiba Saleh, Karsten Krohn, Martin Specht, Michael Hartmann, Katrin Farker, Cord Spreckelsen, Andreas Henkel, Andre Scherag, Danny Ammon

Date Published: 26th Apr 2021

Publication Type: Journal article

In Vivo and In Vitro Characterization of Primary Human Liver Macrophages and Their Inflammatory State
LivSys Transfer - Transfer of the LivSys in vitro system for hepatotoxicity into application

Abstract (Expand)

Liver macrophages (LMs) play a central role in acute and chronic liver pathologies. Investigation of these processes in humans as well as the development of diagnostic tools and new therapeutic strategies … require in vitro models that closely resemble the in vivo situation. In our study, we sought to gain further insight into the role of LMs in different liver pathologies and into their characteristics after isolation from liver tissue. For this purpose, LMs were characterized in human liver tissue sections using immunohistochemistry and bioinformatic image analysis. Isolated cells were characterized in suspension using FACS analyses and in culture using immunofluorescence staining and laser scanning microscopy as well as functional assays. The majority of our investigated liver tissues were characterized by anti-inflammatory LMs which showed a homogeneous distribution and increased cell numbers in correlation with chronic liver injuries. In contrast, pro-inflammatory LMs appeared as temporary and locally restricted reactions. Detailed characterization of isolated macrophages revealed a complex disease dependent pattern of LMs consisting of pro- and anti-inflammatory macrophages of different origins, regulatory macrophages and monocytes. Our study showed that in most cases the macrophage pattern can be transferred in adherent cultures. The observed exceptions were restricted to LMs with pro-inflammatory characteristics.

Authors: Andrea Zimmermann, René Hänsel, Kilian Gemünden, Victoria Kegel-Hübner, Jonas Babel, Hendrik Bläker, Madlen Matz-Soja, Daniel Seehofer, Georg Damm

Date Published: 1st Apr 2021

Publication Type: Journal article

Human Diseases: liver disease, liver cancer

Algorithmic surveillance of ICU patients with acute respiratory distress syndrome (ASIC): protocol for a multicentre stepped-wedge cluster randomised quality improvement strategy
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

INTRODUCTION: The acute respiratory distress syndrome (ARDS) is a highly relevant entity in critical care with mortality rates of 40%. Despite extensive scientific efforts, outcome-relevant therapeutic … measures are still insufficiently practised at the bedside. Thus, there is a clear need to adhere to early diagnosis and sufficient therapy in ARDS, assuring lower mortality and multiple organ failure. METHODS AND ANALYSIS: In this quality improvement strategy (QIS), a decision support system as a mobile application (ASIC app), which uses available clinical real-time data, is implemented to support physicians in timely diagnosis and improvement of adherence to established guidelines in the treatment of ARDS. ASIC is conducted on 31 intensive care units (ICUs) at 8 German university hospitals. It is designed as a multicentre stepped-wedge cluster randomised QIS. ICUs are combined into 12 clusters which are randomised in 12 steps. After preparation (18 months) and a control phase of 8 months for all clusters, the first cluster enters a roll-in phase (3 months) that is followed by the actual QIS phase. The remaining clusters follow in month wise steps. The coprimary key performance indicators (KPIs) consist of the ARDS diagnostic rate and guideline adherence regarding lung-protective ventilation. Secondary KPIs include the prevalence of organ dysfunction within 28 days after diagnosis or ICU discharge, the treatment duration on ICU and the hospital mortality. Furthermore, the user acceptance and usability of new technologies in medicine are examined. To show improvements in healthcare of patients with ARDS, differences in primary and secondary KPIs between control phase and QIS will be tested. ETHICS AND DISSEMINATION: Ethical approval was obtained from the independent Ethics Committee (EC) at the RWTH Aachen Faculty of Medicine (local EC reference number: EK 102/19) and the respective data protection officer in March 2019. The results of the ASIC QIS will be presented at conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: DRKS00014330.

Authors: Gernot Marx, Johannes Bickenbach, Sebastian Johannes Fritsch, Julian Benedict Kunze, Oliver Maassen, Saskia Deffge, Jennifer Kistermann, Silke Haferkamp, Irina Lutz, Nora Kristiana Voellm, Volker Lowitsch, Richard Polzin, Konstantin Sharafutdinov, Hannah Mayer, Lars Kuepfer, Rolf Burghaus, Walter Schmitt, Joerg Lippert, Morris Riedel, Chadi Barakat, André Stollenwerk, Simon Fonck, Christian Putensen, Sven Zenker, Felix Erdfelder, Daniel Grigutsch, Rainer Kram, Susanne Beyer, Knut Kampe, Jan Erik Gewehr, Friederike Salman, Patrick Juers, Stefan Kluge, Daniel Tiller, Emilia Wisotzki, Sebastian Gross, Lorenz Homeister, Frank Bloos, André Scherag, Danny Ammon, Susanne Mueller, Julia Palm, Philipp Simon, Nora Jahn, Markus Loeffler, Thomas Wendt, Tobias Schuerholz, Petra Groeber, Andreas Schuppert

Date Published: 1st Apr 2021

Publication Type: Journal article

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health.
LIFE Adult, oBIG - omics Bioinformatics for Health

Abstract (Expand)

Background: The blood transcriptome is expected to provide a detailed picture of an organism's physiological state with potential outcomes for applications in medical diagnostics and molecular and … epidemiological research. We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration. Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. This method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features. Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identified two major blood transcriptome types where type 1 was found more in men, the elderly, and overweight people and it upregulated genes associated with inflammation and increased heme metabolism, while type 2 was predominantly found in women, younger, and normal weight participants and it was associated with activated immune responses, transcriptional, ribosomal, mitochondrial, and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, aging, and obesity driven by an underlying common pattern, which was associated with the immune response and the increase of inflammatory processes. Conclusions: Machine learning applications for large and heterogeneous omics data provide a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications.

Authors: M. Schmidt, L. Hopp, A. Arakelyan, H. Kirsten, C. Engel, K. Wirkner, K. Krohn, R. Burkhardt, J. Thiery, M. Loeffler, H. Loeffler-Wirth, H. Binder

Date Published: 11th Mar 2021

Publication Type: Journal article

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The Health Atlas - Local Data Hub/Leipzig is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research fields.

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