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Onto-Med Research Group

Overview
Description:

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 with systematically developing axiomatic theories that describe forms, structures, and modes of being at different levels of abstraction and granularity. The roots of formal ontology can be traced back to the philosophical investigations of Aristotle and Plato. Their work was creatively enriched during the early Middle Ages by Islamic philosophers, including Avicenna, renowned as one of the history's greatest thinkers and medical scholars. The Onto-Med Research Group was founded in 2002 and is a result of a collaboration between the Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), University of Leipzig, and the Department of Formal Concepts at the Institute for Informatics (IfI), University of Leipzig.

Programme: This Project is not associated with a Programme

Health Atlas ID: 7Q0CTG2M1H-2

Public web page: http://www.onto-med.de

Organisms: No Organisms specified

Human Diseases: No Human Disease specified

Health Atlas PALs: Heinrich Herre

Project Coordinators: No Project coordinators for this Project

Project start date: 1st Jan 2002

Project Hierarchy

Related items

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
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Person responsible: Christoph Beger

Snapshots: No snapshots

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

Person responsible: Alexandr Uciteli

Snapshots: No snapshots

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

Person responsible: Alexandr Uciteli

Snapshots: No snapshots

Study type: Not specified

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

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

Snapshots: No snapshots

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

Submitter: Alexandr Uciteli

Biological problem addressed: Model Analysis Type

Snapshots: No snapshots

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

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

Snapshots: No snapshots

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

Ontology-Based Modelling of Web Content: Example Leipzig Health Atlas
Onto-Med Research Group

Abstract (Expand)

The realisation of a complex web portal, including the modelling of content, is a challenging process. The contents describe different interconnected entities that form a complex structure. The entities … and their relations have to be systematically analysed, and the content has to be specified and integrated into a content management system (CMS). Ontologies provide a suitable solution for modelling and specifying complex entities and their relations. However, the functionality for automated import of ontologies is not available in current content management systems. In order to describe the content of a web portal, we developed an ontology. Based on this ontology, we implemented a pipeline that allows the specification of the portal’s content and its import into the CMS Drupal. Our method is generic. It enables the development of web portals with the focus on a suitable representation of structured knowledge (entities, their properties and relations). Furthermore, it makes it possible to represent existing ontologies in such a way that their content can be understood by users without knowledge of ontologies and their semantics. Our approach has successfully been applied in building the LHA (Leipzig Health Atlas) portal, which provides access to metadata, data, publications and methods from various research projects at the University of Leipzig.

Authors: A. Uciteli, C. Beger, C. Rillich, F. A. Meineke, M. Loffler, H. Herre

Date Published: 2018

Publication Type: InBook

Light-Weighted Automatic Import of Standardized Ontologies into the Content Management System Drupal.
Onto-Med Research Group

Abstract (Expand)

The amount of ontologies, which are utilizable for widespread domains, is growing steadily. BioPortal alone, embraces over 500 published ontologies with nearly 8 million classes. In contrast, the vast … informative content of these ontologies is only directly intelligible by experts. To overcome this deficiency it could be possible to represent ontologies as web portals, which does not require knowledge about ontologies and their semantics, but still carries as much information as possible to the end-user. Furthermore, the conception of a complex web portal is a sophisticated process. Many entities must be analyzed and linked to existing terminologies. Ontologies are a decent solution for gathering and storing this complex data and dependencies. Hence, automated imports of ontologies into web portals could support both mentioned scenarios. The Content Management System (CMS) Drupal 8 is one of many solutions to develop web presentations with less required knowledge about programming languages and it is suitable to represent ontological entities. We developed the Drupal Upper Ontology (DUO), which models concepts of Drupal's architecture, such as nodes, vocabularies and links. DUO can be imported into ontologies to map their entities to Drupal's concepts. Because of Drupal's lack of import capabilities, we implemented the Simple Ontology Loader in Drupal (SOLID), a Drupal 8 module, which allows Drupal administrators to import ontologies based on DUO. Our module generates content in Drupal from existing ontologies and makes it accessible by the general public. Moreover Drupal offers a tagging system which may be amplified with multiple standardized and established terminologies by importing them with SOLID. Our Drupal module shows that ontologies can be used to model content of a CMS and vice versa CMS are suitable to represent ontologies in a user-friendly way. Ontological entities are presented to the user as discrete pages with all appropriate properties, links and tags.

Authors: C. Beger, A. Uciteli, H. Herre

Date Published: 9th Sep 2017

Publication Type: Journal article

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

Core Ontology of Phenotypes. Contribute to Onto-Med/COP development by creating an account on GitHub.

Creators: Heinrich Herre, Alexandr Uciteli, Christoph Beger

Submitter: Christoph Beger

External Link
Simple Ontology Loader in Drupal (SOLID)
Onto-Med Research Group

This is a Drupal 8 Module to import nodes and taxonomies into Drupal, using the available API. The module is capable of importing a JSON or OWL file. Idea is to enable users to upload a file containing all information about nodes and their relations. The module will then import all contained information with the Drupal 8 API. Nodes can be specified as "articles" or any other custom node type. The node classification becomes one (or multiple) hierachical vocabulary. Supported import formats are
...

Creator: Christoph Beger

Submitter: Christoph Beger

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

Presentation on JOWO/ODLS 2019 in Graz

Creators: Alexandr Uciteli, Christoph Beger

Submitter: Christoph Beger

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