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13 Publications visible to you, out of a total of 13
The Leipzig Health Atlas-An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online.
LHA - Leipzig Health Atlas

Abstract (Expand)

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.

Authors: T. Kirsten, F. A. Meineke, H. Loeffler-Wirth, C. Beger, A. Uciteli, S. Staubert, M. Lobe, R. Hansel, F. G. Rauscher, J. Schuster, T. Peschel, H. Herre, J. Wagner, S. Zachariae, C. Engel, M. Scholz, E. Rahm, H. Binder, M. Loeffler

Date Published: 3rd Aug 2022

Publication Type: Journal article

Towards an ontology-based phenotypic query model
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Clinical research based on data from patient or study data management systems plays an important role in transferring basic findings into the daily practices of physicians. To support study recruitment, … diagnostic processes, and risk factor evaluation, search queries for such management systems can be used. Typically, the query syntax as well as the underlying data structure vary greatly between different data management systems. This makes it difficult for domain experts (e.g., clinicians) to build and execute search queries. In this work, the Core Ontology of Phenotypes is used as a general model for phenotypic knowledge. This knowledge is required to create search queries that determine and classify individuals (e.g., patients or study participants) whose morphology, function, behaviour, or biochemical and physiological properties meet specific phenotype classes. A specific model describing a set of particular phenotype classes is called a Phenotype Specification Ontology. Such an ontology can be automatically converted to search queries on data management systems. The methods described have already been used successfully in several projects. Using ontologies to model phenotypic knowledge on patient or study data management systems is a viable approach. It allows clinicians to model from a domain perspective without knowing the actual data structure or query language.

Authors: Christoph Beger, Franz Matthies, Ralph Schäfermeier, Toralf Kirsten, Heinrich Herre, Alexandr Uciteli

Date Published: 1st May 2022

Publication Type: Journal article

Ontological Modelling and Execution of Phenotypic Queries in the Leipzig Health Atlas.
LHA - Leipzig Health Atlas, Onto-Med Research Group, 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: A. Uciteli, C. Beger, J. Wagner, A. Kiel, F. A. Meineke, S. Staubert, M. Lobe, R. Hansel, J. Schuster, T. Kirsten, H. Herre

Date Published: 24th 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

Ontological representation, classification and data-driven computing of phenotypes.
LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

BACKGROUND: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and … epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term 'phenotype' has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case 'phenotype pipeline' (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. RESULTS: In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. CONCLUSIONS: We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies.

Authors: A. Uciteli, C. Beger, T. Kirsten, F. A. Meineke, H. Herre

Date Published: 21st Dec 2020

Publication Type: Journal article

Ontological representation, classification and data-driven computing of phenotypes
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

BACKGROUND: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and … epidemiological studies. The development of computable phenotype algorithms to solve these tasks is a challenging problem, caused by various reasons. Firstly, the term ’phenotype’ has no generally agreed definition and its meaning depends on context. Secondly, the phenotypes are most commonly specified as non-computable descriptive documents. Recent attempts have shown that ontologies are a suitable way to handle phenotypes and that they can support clinical research and decision making. The SMITH Consortium is dedicated to rapidly establish an integrative medical informatics framework to provide physicians with the best available data and knowledge and enable innovative use of healthcare data for research and treatment optimisation. In the context of a methodological use case ’phenotype pipeline’ (PheP), a technology to automatically generate phenotype classifications and annotations based on electronic health records (EHR) is developed. A large series of phenotype algorithms will be implemented. This implies that for each algorithm a classification scheme and its input variables have to be defined. Furthermore, a phenotype engine is required to evaluate and execute developed algorithms. RESULTS: In this article, we present a Core Ontology of Phenotypes (COP) and the software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model, classify and compute phenotypes from already available data. Our solution includes an enhanced iterative reasoning process combining classification tasks with mathematical calculations at runtime. The ontology as well as the reasoning method were successfully evaluated with selected phenotypes including SOFA score, socio-economic status, body surface area and WHO BMI classification based on available medical data. CONCLUSIONS: We developed a novel ontology-based method to model phenotypes of living beings with the aim of automated phenotype reasoning based on available data. This new approach can be used in clinical context, e.g., for supporting the diagnostic process, evaluating risk factors, and recruiting appropriate participants for clinical and epidemiological studies.

Authors: Alexandr Uciteli, Christoph Beger, Toralf Kirsten, Frank A Meineke, Heinrich Herre

Date Published: 1st Dec 2020

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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