SMITH - Smart Medical Information Technology for Healthcare
OverviewMedical 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 Hospital and the University Medical Center Rostock are preparing the structure.
Programme: MII - Medical Informatics Initiative
LHA ID: 81CN2DP7WT-9
Funding codes:- 01ZZ1803A
Public web page: https://www.smith.care/
Human Diseases: No Human Disease specified
Health Atlas - Local Data Hub/Leipzig PALs: Markus Löffler
Project Coordinators: No Project coordinators for this Project
Project start date: 1st Jan 2018
Related items
One of the challenges of Phenotypingt is that too little clinical information is available as machine-readable data sets. Admission letters, findings and operating room reports in particular contain valuable information such as diagnoses, medications, side effects and laboratory data that can only be extracted using methods of natural language processing and semantic text analysis methods. Natural Language Processing (NLP) is used to process documents from the Hospital Information System (HIS). ...
PheP is a platform that enables clinical researchers to work together with statisticians and computer scientists in interdisciplinary collaboration to pursue scientific issues that previously seemed economically and technologically unthinkable. For this purpose, it is necessary to build data sets that can be used for clinical-epidemiological and health-economic issues.
From phenotypes, i.e. determinable characteristics of patients, further characteristics can be derived and provided via phenotyping. ...
Submitter: Alexandr Uciteli
Studies: Ontological Modelling of Basic Eligibility Criteria, Ontological Modelling of Type 2 Diabetes Mellitus (T2DM) Phenotype, PhenoMan Evaluation with Synthetic FHIR Data
Resources: Basic Eligibility Criteria of an Example Blood Pressure Study, Ontological Modelling of T2DM Phenotype using Phenotype Manager (PhenoMan), PhenoMan Evaluation - Synthetic FHIR Data
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
Submitter: Alexandr Uciteli
Investigation: Ontology-based Phenotyping
Resources: Basic Eligibility Criteria of an Example Blood Pressure Study
Study type: Not specified
Submitter: Alexandr Uciteli
Investigation: Ontology-based Phenotyping
Resources: Ontological Modelling of T2DM Phenotype using Phenotype Manager (PhenoMan)
Study type: Not specified
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
Submitter: Alexandr Uciteli
Biological problem addressed: Model Analysis Type
Investigation: Ontology-based Phenotyping
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
Abstract (Expand)
Editor:
Date Published: 17th Aug 2022
Publication Type: InProceedings
Abstract (Expand)
Editor: Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Date Published: 19th Jun 2022
Publication Type: Journal article
Abstract (Expand)
Editor:
Date Published: 27th May 2021
Publication Type: Journal article
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
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
Abstract (Expand)
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
Abstract (Expand)
Authors: A. Uciteli, C. Beger, T. Kirsten, F. A. Meineke, H. Herre
Date Published: 21st Dec 2020
Publication Type: Journal article
Abstract
Authors: Udo Hahn, Michel Oleynik
Date Published: 21st Aug 2020
Publication Type: Journal article
Abstract
Authors: Miriam Kesselmeier, Norbert Benda, André Scherag
Date Published: 14th Aug 2020
Publication Type: Journal article
Abstract
Authors: Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, Yiqun Liu, Erik Faessler, Michel Oleynik, Udo Hahn
Date Published: 25th Jul 2020
Publication Type: InProceedings
T2DM Phenotype Algorithm Specification Ontology (PASO) developed using PhenoMan
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The tabular representation of the T2DM phenotype algorithm generated by PhenoMan using the T2DM ontology
Example Reasoner Report generated by PhenoMan
Example Reasoner Report generated by PhenoMan
Example Reasoner Report generated by PhenoMan
Example Reasoner Report generated by PhenoMan
Example Reasoner Report generated by PhenoMan
The decision tree was generated by PhenoMan in GraphML format and was processed using an automatic layout of the yEd Graph Editor. Nonetheless, it is also possible to generate the complete tree as image in PNG format.
Creator: Christina Lohr
Submitter: Christina Lohr
Model type: Not specified
Model format: Not specified
Environment: Not specified
Presentation on JOWO/ODLS 2019 in Graz
Creators: Alexandr Uciteli, Christoph Beger
Submitter: Christoph Beger
As head of the Data Integration Center at Jena University Hospital, Dr. Danny Ammon is active in the areas of standardization, processing and communication of medical documentation for healthcare and biomedical research.
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/0000-0002-1166-0368
Profile image source: Swen Reichhold
Projects: SMITH - Smart Medical Information Technology for Healthcare
Institutions: Averbis GmbH Freiburg
Projects: SMITH - Smart Medical Information Technology for Healthcare
Institutions: Universitätsklinikum Essen
Projects: SMITH - Smart Medical Information Technology for Healthcare
Institutions: Universitätsklinikum Jena
Projects: SMITH - Smart Medical Information Technology for Healthcare
Institutions: Friedrich-Schiller-Universität Jena
Projects: LHA - Leipzig Health Atlas, LIFE Adult, LIFE - Leipzig Research Center for Civilization Diseases, LIFE HNC - Head and Neck Cancer Group, LIFE Heart, MMML - Molecular mechanisms in malignant lymphoma, GLA - German Lymphoma Alliance, MMML Demonstrators - Molecular Mechanisms in Malignant Lymphomas - Demonstrators of Personalized Medicine, HaematoOpt - Individualized model-based managing of the next-cycle thrombopenia of CHOEP/CHOP treated patients based on platelets dynamics during the previous cycles, e:Med, GC-HBOC - German Consortium for Hereditary Breast and Ovarian Cancer, GC-HNPCC - German Consortium for Hereditary Non-Polyposis Colorectal Cancer, MMML-MYC-SYS, NLP4CR - Natural Language Processing for Clinical Research, Genetical Statistics and Systems Biology, SepNet - German Competence Network Sepsis, LIFE Child, HNPCC-Sys - Genomic and transcriptomic heterogeneity of colorectal tumours arising in Lynch syndrome, GGN - German Glioma Network, CAPSys - Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome, CapSys - Systems Medicine of Community Acquired Pneumonia, ProstataCA, HaematoSys - Systems biology of haematopoiesis and haematopoietic neoplasia, SMITH - Smart Medical Information Technology for Healthcare, Task Force COVID-19 Leipzig, NFDI4Health, POLAR - Polypharmacy, Drug Interactions, Risks, Management of health information systems, LivSys Transfer - Transfer of the LivSys in vitro system for hepatotoxicity into application, Project Test Demonstrator, Fundus photography as tool for analysis of eyes of subjects with diabetes, Clinical Trials Leipzig, NFDI4Health - TA3 Services
Institutions: Institute for Medical Informatics, Statistics and Epidemiology (IMISE)
https://orcid.org/0000-0001-8344-0658
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
The Joint Ontology WOrkshops (JOWO) is a venue of workshops that, together, address a wide spectrum of topics related to ontology research, ranging from Cognitive Science to Knowledge Representation, Natural Language Processing, Artificial Intelligence, Logic, Philosophy, and Linguistics.
Country: 
Austria
City: Graz
