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Created: 9th Mar 2020 at 12:34
Last updated: 29th Jun 2020 at 14:50
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The Leipzig Health Atlas (LHA) 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 in which our team has scientific and clinical expertise.
Programme: i:DSem - Integrative Datensemantik in der Systemmedizin
Public web page: https://www.health-atlas.de
Start date: 1st Apr 2016
End date: 28th Feb 2021
Organisms: Homo sapiens
Medical data routinely generated in everyday clinical practice is processed and made available to medical research in a standardized form. Patients benefit from reliable research results, more precise diagnoses and better treatments. In order to link data from care and research, the participating university hospitals in Aachen, Bonn, Essen, Halle, Hamburg, Jena and Leipzig have established sustainable Data Integration Centers. The network partners Ruhr University Bochum, the Düsseldorf University ...
Programme: MII - Medical Informatics Initiative
Public web page: https://www.smith.care/
Start date: 1st Jan 2018
Organisms: Homo sapiens
The Onto-Med Research Group conducts basic research in formal ontology, designs formal tools for constructing and managing ontologies and develops top level ontologies as well as domain and core ontologies for medicine, bio-medicine and biology, but also for other fields. The Onto-Med group uses an interdisciplinary approach, combining methods from logic, computer science, philosophy and cognitive linguistics. The Onto-Med group considers Formal Ontology as an evolving science which is concerned ...
Programme: This Project is not associated with a Programme
Public web page: http://www.onto-med.de
Start date: 1st Jan 2002
Organisms: Not specified
We evaluated if the PhenoMan returns correct/complete result sets and if it is working with real data. To simulate a FHIR health data store with real data we used Synthea(TM) to generate a large data set and imported it into a HAPI FHIR JPA Server. Based on the synthetic data set we developed ten example queries with different structure and complexity with PhenoMan and SQL. We compared the results of the queries in means of execution time and equality of results.
The detailed steps of the evaluation ...
Submitter: Christoph Beger
Investigation: Ontology-based Phenotyping
Resources: PhenoMan Evaluation - Synthetic FHIR Data
Study type: Not specified
Snapshots: No snapshots
Submitter: Alexandr Uciteli
Investigation: Ontology-based Phenotyping
Resources: Basic Eligibility Criteria of an Example Blood Pressure Study
Study type: Not specified
Snapshots: No snapshots
Submitter: Alexandr Uciteli
Investigation: Ontology-based Phenotyping
Resources: Ontological Modelling of T2DM Phenotype using Phenotype Manager (PhenoMan)
Study type: Not specified
Snapshots: No snapshots
This assay bundles all synthetic FHIR data of the PhenoMan evaluation. The data arised from a subset of a Synthea(TM) generated data set. We truncated some resource types like Encounter and Provider to reduce the size of the data set and to speed up the import in a FHIR health data store.
Submitter: Christoph Beger
Resource type: Result Dataset of Clinical Study
Technology type: Technology Type
Investigation: Ontology-based Phenotyping
Human Diseases: asthma, bronchial disease, hypertension, obesity
Data files: PhenoMan Evaluation - AllergyIntolerance FHIR R..., PhenoMan Evaluation - Condition FHIR Resources, PhenoMan Evaluation - Observation FHIR Resources, PhenoMan Evaluation - Patient FHIR Resources
Snapshots: No snapshots
Submitter: Alexandr Uciteli
Biological problem addressed: Model Analysis Type
Investigation: Ontology-based Phenotyping
Human Diseases: No human diseases
Models: No Models
Data files: Eligibility Criteria Ontology for an Example Bl...
Snapshots: No snapshots
We modelled the algorithm for determining Type 2 Diabetes Mellitus (T2DM) cases presented by PheKB.org using Phenotype Manager (PhenoMan).
Submitter: Alexandr Uciteli
Biological problem addressed: Model Analysis Type
Investigation: Ontology-based Phenotyping
Human Diseases: diabetes mellitus
Models: No Models
Data files: T2DM Case 1 Reasoner Report, T2DM Case 2 Reasoner Report, T2DM Case 3 Reasoner Report, T2DM Case 4 Reasoner Report, T2DM Case 5 Reasoner Report, T2DM Graphical Representation, T2DM Ontology, T2DM Tabular Representation
Snapshots: No snapshots
Abstract (Expand)
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
PubMed ID: 34042877
Citation: Stud Health Technol Inform. 2021 May 24;278:66-74. doi: 10.3233/SHTI210052.
Abstract (Expand)
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
DOI: 10.3205/mibe000219
Citation: Uciteli A, Beger C, Wagner J, Kirsten T, Meineke FA, Staubert S, Lobe M, Herre H: Ontological modelling and FHIR Search based representation of basic eligibility criteria. GMS Med Inform Biom Epidemiol 2021;17(2):Doc05.
Abstract (Expand)
Authors: A. Uciteli, C. Beger, T. Kirsten, F. A. Meineke, H. Herre
Date Published: 21st Dec 2020
Publication Type: Journal article
PubMed ID: 33349245
Citation: J Biomed Semantics. 2020 Dec 21;11(1):15. doi: 10.1186/s13326-020-00230-0.
Abstract (Expand)
Authors: Alexandr Uciteli, Christoph Beger, Toralf Kirsten, Frank A. Meineke, Heinrich Herre
Date Published: 20th Dec 2019
Publication Type: InProceedings
Citation: CEUR Workshop Proceedings. 2019 Sep;2570. issn: 1613-0073.
T2DM Phenotype Algorithm Specification Ontology (PASO) developed using PhenoMan
Investigations: Ontology-based Phenotyping
Studies: Ontological Modelling of Type 2 Diabetes Mellit...
Resources: Ontological Modelling of T2DM Phenotype using P...
The tabular representation of the T2DM phenotype algorithm generated by PhenoMan using the T2DM ontology
Investigations: Ontology-based Phenotyping
Studies: Ontological Modelling of Type 2 Diabetes Mellit...
Resources: Ontological Modelling of T2DM Phenotype using P...
This data file contains FHIR bundles of observation resources, which were used for the evaluation of the PhenoMan. Originally the observation data were generated with Synthea(TM) and truncated to reduce overall size and import times into a HAPI FHIR JPA Server. Please import the patient resources prior to the observations.
This data file contains 8,026,380 observations.
Creators: Alexandr Uciteli, Christoph Beger
Submitter: Christoph Beger
Data file type: Clinical Data
Human Diseases: obesity, bronchial disease, asthma, hypertension
Investigations: Ontology-based Phenotyping
Studies: PhenoMan Evaluation with Synthetic FHIR Data
Resources: PhenoMan Evaluation - Synthetic FHIR Data
This data file contains FHIR bundles of patient resources, which were used for the evaluation of the PhenoMan. Originally the patient data were generated with Synthea(TM) and truncated to reduce overall size and import times into a HAPI FHIR JPA Server.
This data file contains 66,018 patients.
Creators: Alexandr Uciteli, Christoph Beger
Submitter: Christoph Beger
Data file type: Clinical Data
Human Diseases: obesity, bronchial disease, asthma, hypertension
Investigations: Ontology-based Phenotyping
Studies: PhenoMan Evaluation with Synthetic FHIR Data
Resources: PhenoMan Evaluation - Synthetic FHIR Data
This data file contains FHIR bundles of allergy intolerance resources, which were used for the evaluation of the PhenoMan. Originally the allergy intolerance data were generated with Synthea(TM) and truncated to reduce overall size and import times into a HAPI FHIR JPA Server. Please import the patient resources prior to the allergy intolerances.
This data file contains 563 allergy intolerances.
Creators: Alexandr Uciteli, Christoph Beger
Submitter: Christoph Beger
Data file type: Clinical Data
Human Diseases: obesity, bronchial disease, asthma, hypertension
Investigations: Ontology-based Phenotyping
Studies: PhenoMan Evaluation with Synthetic FHIR Data
Resources: PhenoMan Evaluation - Synthetic FHIR Data
This data file contains FHIR bundles of condition resources, which were used for the evaluation of the PhenoMan. Originally the condition data were generated with Synthea(TM) and truncated to reduce overall size and import times into a HAPI FHIR JPA Server. Please import the patient resources prior to the conditions.
This data file contains 139,763 conditions.
Creators: Alexandr Uciteli, Christoph Beger
Submitter: Christoph Beger
Data file type: Clinical Data
Human Diseases: obesity, bronchial disease, asthma, hypertension
Investigations: Ontology-based Phenotyping
Studies: PhenoMan Evaluation with Synthetic FHIR Data
Resources: PhenoMan Evaluation - Synthetic FHIR Data
Investigations: Ontology-based Phenotyping
Studies: Ontological Modelling of Basic Eligibility Crit...
Resources: Basic Eligibility Criteria of an Example Blood ...
Example Reasoner Report generated by PhenoMan
Investigations: Ontology-based Phenotyping
Studies: Ontological Modelling of Type 2 Diabetes Mellit...
Resources: Ontological Modelling of T2DM Phenotype using P...
Example Reasoner Report generated by PhenoMan
Investigations: Ontology-based Phenotyping
Studies: Ontological Modelling of Type 2 Diabetes Mellit...
Resources: Ontological Modelling of T2DM Phenotype using P...
Example Reasoner Report generated by PhenoMan
Investigations: Ontology-based Phenotyping
Studies: Ontological Modelling of Type 2 Diabetes Mellit...
Resources: Ontological Modelling of T2DM Phenotype using P...
Projects: LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare, Task Force COVID-19 Leipzig, NFDI4Health, LIFE Child, LIFE - Leipzig Research Center for Civilization Diseases, Project Test Demonstrator
Institutions: Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Universitätsklinikum Leipzig, AöR, invalid
https://orcid.org/0000-0002-1166-0368Expertise: data integration, ruby, r, java, perl
Profile image source: Swen Reichhold
Within the framework of the funding measure "i:DSem - Integrative Data Semantics in Systems Medicine", the Federal Ministry of Education and Research supports research projects that take up this approach. Interdisciplinary research projects are to create the basis so that in the future, for example, the attending physician will have application-oriented computer programmes at his or her disposal that will enable him or her to access all clinically relevant data. This should significantly support ...
Projects: LHA - Leipzig Health Atlas
The medical informatics initiative was created to close the gap between research and healthcare. All of Germany’s university hospitals have joined forces with research institutions, businesses, health insurers, and patient advocacy groups to create a framework that harnesses research findings to the direct benefit of patients. The German Federal Ministry of Education and Research (BMBF) is investing around 160 million euros in the programme through 2021. The digitisation of medicine is creating ...
Projects: Methodical Use Case PheP, SMITH - Smart Medical Information Technology for Healthcare, POLAR - Polypharmacy, Drug Interactions, Risks