Alexandr Uciteli

About Alexandr Uciteli:
No description specified

LHA ID: 7Y2WQT09JY-4

Location:  Germany

ORCID: https://orcid.org/0000-0001-9558-5352

Joined: 9th Mar 2020

Expertise: Not specified

Tools: Not specified

project_administrator
help Their tags

This person has not yet tagged anything.

Related items

LHA - Leipzig Health Atlas

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

SMITH - Smart Medical Information Technology for Healthcare

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

Onto-Med Research Group

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

Phenotype Knowledge Model
Onto-Med Research Group

The aim of this investigation is to develop a methodology to model and express phenotype classes. These models can be used to generate queries to search for individuals (patients or study participants) with specific phenotypes.

Submitter: Christoph Beger

Studies: Ontology-based Phenotype Knowledge Model

Resources: Example Phenotype Specification Ontology for Inclusion Criteria Hyperten...

Ontology-based Phenotyping
SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group
No description specified

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

Ontology-based Phenotype Knowledge Model
Onto-Med Research Group

In this study we used the Core Ontology of Phenotypes as general phenotypic model to represent phenotype knowledge. The phenotypes are modelled in so called Phenotype Specification Ontologies.

Submitter: Christoph Beger

Investigation: Phenotype Knowledge Model

Resources: Example Phenotype Specification Ontology for Inclusion Criteria Hyperten...

Study type: Not specified

PhenoMan Evaluation with Synthetic FHIR Data
SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group

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

Ontological Modelling of Basic Eligibility Criteria
SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group
No description specified

Submitter: Alexandr Uciteli

Investigation: Ontology-based Phenotyping

Resources: Basic Eligibility Criteria of an Example Blood Pressure Study

Study type: Not specified

Ontological Modelling of Type 2 Diabetes Mellitus (T2DM) Phenotype
SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group
No description specified

Submitter: Alexandr Uciteli

Investigation: Ontology-based Phenotyping

Resources: Ontological Modelling of T2DM Phenotype using Phenotype Manager (PhenoMan)

Study type: Not specified

Example Phenotype Specification Ontology for Inclusion Criteria Hypertension and Obesity
Onto-Med Research Group

Example study for people with hypertension or obesity as inclusion criteria. This ontology uses the Core Ontology of Phenotypes as a general phenotypic model.

Submitter: Christoph Beger

Biological problem addressed: Model Analysis Type

Investigation: Phenotype Knowledge Model

Study: Ontology-based Phenotype Knowledge Model

PhenoMan Evaluation - Synthetic FHIR Data
SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group

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

Study: PhenoMan Evaluation with Synthetic FHIR Data

Basic Eligibility Criteria of an Example Blood Pressure Study
SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group
No description specified

Submitter: Alexandr Uciteli

Biological problem addressed: Model Analysis Type

Investigation: Ontology-based Phenotyping

Study: Ontological Modelling of Basic Eligibility Crit...

Ontological Modelling of T2DM Phenotype using Phenotype Manager (PhenoMan)
SMITH - Smart Medical Information Technology for Healthcare, LHA - Leipzig Health Atlas, Onto-Med Research Group

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

Study: Ontological Modelling of Type 2 Diabetes Mellit...

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

Ontological Modelling and Reasoning of Phenotypes
LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

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 optimization. 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. In this article we present a Core Ontology of Phenotypes (COP) and a software Phenotype Manager (PhenoMan), which implements a novel ontology-based method to model and calculate phenotypes. 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 based on different phenotypes (including SOFA score, socioeconomic status, body surface area and WHO BMI classification) and several data sets.

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

Date Published: 20th Dec 2019

Publication Type: InProceedings

Der Leipzig Health Atlas
LHA - Leipzig Health Atlas

Abstract (Expand)

Die Notwendigkeit des Managements von Forschungsdaten ist von der Forschungscommunity erkannt – Sponsoren, Gesetzgeber, Verlage erwarten und fördern die Einhaltung der guten wissenschaftlichen Praxis, … was nicht nur die Archivierung umfasst, sondern auch die Verfügbarkeit von Forschungsdaten- und ergebnissen im Sinne der FAIR-Prinzipien. Der Leipzig Health Atlas (LHA) ist ein Projekt zur Präsentation und zum Austausch eines breiten Spektrums von Publikationen, (bio) medizinischen Daten (z.B. klinisch, epidemiologisch, molekular), Modellen und Tools z.B. zur Risikoberechnung in der Gesundheitsforschung. Die Verbundpartner decken hierbei einen breiten Bereich wissenschaftlicher Disziplinen ab, beginnend von medizinischer Systembiologie über klinische und epidemiologische Forschung bis zu ontologischer und dynamischer Modellierung. Derzeit sind 18 Forschungskonsortien beteiligt (u.a. zu den Domänen Lymphome, Gliome, Sepsis, Erblicher Darm- und Brustkrebs), die Daten aus klinischen Studien, Patientenkohorten, epidemiologischen Kohorten, teilweise mit umfangreichen molekularen und genetischen Profilen, sammeln. Die Modellierung umfasst algorithmische Phänotypklassifizierung, Risikovorhersage und Krankheitsdynamik. Wir konnten in einer ersten Entwicklungsphase zeigen, dass unsere webbasierte Plattform geeignet ist, um (1) Methoden zur Verfügung zu stellen, um individuelle Patientendaten aus Publikationen für eine Weiternutzung zugänglich zu machen, (2) algorithmische Werkzeuge zur Phänotypisierung und Risikoprofilerstellung zu präsentieren, (3) Werkzeuge zur Durchführung dynamischer Krankheits- und Therapiemodelle interaktiv verfügbar zu machen und (4) strukturierte Metadaten zu quantitativen und qualitativen Merkmalen bereit zu stellen. Die semantische Datenintegration liefert hierzu die Technologien (Ontologien und Datamining Werkzeuge) für die (semantische) Datenintegration und Wissensanreicherung. Darüber hinaus stellt sie Werkzeuge zur Verknüpfung eigener Daten, Analyseergebnisse, öffentlich zugänglicher Daten- und Metadaten-Repositorien sowie zur Verdichtung komplexer Daten zur Verfügung. Eine Arbeitsgruppe zur Applikationsentwicklung und –validierung entwickelt innovative paradigmatische Anwendungen für (1) die klinische Entscheidungsfindung für Krebsstudien, die genetische Beratung, für Risikovorhersagemodelle sowie Gewebe- und Krankheitsmodelle und (2) Anwendungen (sog. Apps), die sich auf die Charakterisierung neuer Phänotypen (z.B. ‚omics‘-Merkmale, Körpertypen, Referenzwerte) aus epidemiologischen Studien konzentrieren. Diese Anwendungen werden gemeinsam mit klinischen Experten, Genetikern, Systembiologen, Biometrikern und Bioinformatikern spezifiziert. Der LHA stellt Integrationstechnologie bereit und implementiert die Anwendungen für die User Communities unter Verwendung verschiedener Präsentationswerkzeuge bzw. Technologien (z.B. R-Shiny, i2b2, Kubernetes, SEEK). Dazu ist es erforderlich, die Daten und Metadaten vor dem Hochladen zu kuratieren, Erlaubnisse der Datenbesitzer einzuholen, die erforderlichen Datenschutzkriterien zu berücksichtigen und semantische Annotationen zu überprüfen. Zudem werden die zugelieferten Modellalgorithmen in einer qualitätsgesicherten Weise aufbereitet und, soweit anwendbar, online interaktiv zur Verfügung gestellt. Der LHA richtet sich insbesondere an die Zielgruppen Kliniker, Epidemiologen, Molekulargenetiker, Humangenetiker, Pathologen, Biostatistiker und Modellierer ist aber unter www.healthatlas.de öffentlich zugänglich – aus rechtlichen Gründen erfordert der Zugriff auf bestimmte Applikationen und Datensätze zusätzliche Autorisierung. Das Projekt wird über das BMBF Programm i:DSem (Integrative Datensemantik für die Systemmedizin, Förderkennzeichen 031L0026) gefördert.

Authors: F. A. Meineke, Sebastian Stäubert, Matthias Löbe, C. Beger, René Hänsel, A. Uciteli, H. Binder, T. Kirsten, M. Scholz, H. Herre, C. Engel, Markus Löffler

Date Published: 19th Sep 2019

Publication Type: Misc

Design and Concept of the SMITH Phenotyping Pipeline.
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Phenotyping means the determination of clinical relevant phenotypes, e.g. by classification or calculation based on EHR data. Within the German Medical Informatics Initiative, the SMITH consortium is … working on the implementation of a phenotyping pipeline. to extract, structure and normalize information from the EHR data of the hospital information systems of the participating sites; to automatically apply complex algorithms and models and to enrich the data within the research data warehouses of the distributed data integration centers with the computed results. Here we present the overall picture and essential building blocks and workflows of this concept.

Authors: F. A. Meineke, S. Staubert, M. Lobe, A. Uciteli, M. Loffler

Date Published: 3rd Sep 2019

Publication Type: Journal article

OWL representation of the Phenotype Specification Ontology
Onto-Med Research Group
No description specified

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

Submitter: Christoph Beger

Data file type: Not specified

Human Diseases: obesity, hypertension

Download
T2DM Ontology
LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

T2DM Phenotype Algorithm Specification Ontology (PASO) developed using PhenoMan

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Download
T2DM Tabular Representation
LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

The tabular representation of the T2DM phenotype algorithm generated by PhenoMan using the T2DM ontology

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Download
PhenoMan Evaluation - Observation FHIR Resources
Onto-Med Research Group

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

Download
PhenoMan Evaluation - Patient FHIR Resources
Onto-Med Research Group

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

Download
PhenoMan Evaluation - AllergyIntolerance FHIR Resources
Onto-Med Research Group

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

Download
PhenoMan Evaluation - Condition FHIR Resources
Onto-Med Research Group

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

Download
Eligibility Criteria Ontology for an Example Blood Pressure Study
Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare
No description specified

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Download
T2DM Case 5 Reasoner Report
LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Example Reasoner Report generated by PhenoMan

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Download
T2DM Case 4 Reasoner Report
LHA - Leipzig Health Atlas, Onto-Med Research Group, SMITH - Smart Medical Information Technology for Healthcare

Example Reasoner Report generated by PhenoMan

Creator: Alexandr Uciteli

Submitter: Alexandr Uciteli

Data file type: Not specified

Download
2 hidden items
Core Ontology of Phenotyping
Onto-Med Research Group, LHA - Leipzig Health Atlas

The Core Ontology of Phenotyping (COP) defines core entities relevant to the modelling of phenotypic knowledge and the development of model-driven phenotyping software (such as the TOP Framework). The COP is founded on GFO-light.

Creators: Heinrich Herre, Alexandr Uciteli, Christoph Beger

Submitter: Christoph Beger

External Link
Terminology- and Ontology-based Phenotyping Framework
Onto-Med Research Group

The TOP Framework enables users to model phenotypes according to the Core Ontology of Phenotypes. It also includes a custom reasoning engine and query service for classification of individual data and searching in data repositories (e.g., Health Data Stores).

Creators: Christoph Beger, Alexandr Uciteli, Franz Matthies

Submitter: Christoph Beger

External Link
Ontological Modelling and Reasoning of Phenotypes
LHA - Leipzig Health Atlas, SMITH - Smart Medical Information Technology for Healthcare, Onto-Med Research Group

Presentation on JOWO/ODLS 2019 in Graz

Creators: Alexandr Uciteli, Christoph Beger

Submitter: Christoph Beger

Download
i:DSem - Integrative Datensemantik in der Systemmedizin

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

Web page: https://www.gesundheitsforschung-bmbf.de/de/i-dsem-integrative-datensemantik-in-der-systemmedizin-3367.php

MII - Medical Informatics Initiative

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, WP 1.5 of POLAR_MI (Polypharmacy, Drug Interactions, Risks) - Development of risk models, POLAR - Polypharmacy, Drug Interactions, Risks

Web page: https://www.medizininformatik-initiative.de/en

Navigate

Health Atlas - Local Data Hub/Leipzig

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.

Registered Repository

re3data.org Badge
Powered by
 (v.1.13.0-master)
Health Atlas | Funding and Programmes | Credits | Terms & Conditions | Privacy Policy | Imprint
Copyright © 2008 - 2021 The University of Manchester and HITS gGmbH
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig

By continuing to use this site you agree to the use of cookies