Ontology-based Phenotyping

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 ...

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

Example Reasoner Report generated by PhenoMan

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 ...

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 ...