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.

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.

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 ...

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

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.

We modelled the algorithm for determining Type 2 Diabetes Mellitus (T2DM) cases presented by PheKB.org using Phenotype Manager (PhenoMan).

T2DM Phenotype Algorithm Specification Ontology (PASO) developed using PhenoMan

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

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.

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.

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.

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.

Example Reasoner Report generated by PhenoMan

Example Reasoner Report generated by PhenoMan

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.

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).

The General Formal Ontology is a top-level ontology for conceptual modeling. It includes elaborations of categories like objects, processes, time and space, properties, relations, roles, functions, facts, and situations.

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 ...

Presentation on JOWO/ODLS 2019 in Graz