Pregnancy Ontology

Pregnancy Ontology

 

The pregnancy ontology  assists to identify of pregnancy episodes cases from routine data. This ontology has been validated and optimised by using the the Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) sentinel network database.

The pregnancy ontology semantically integrates concepts related pregnancies through mappings of pregnancy ontology terms  to Read ver2 and CTV3.

Pregnancy Ontology Toolkit

The pregnancy ontology toolkit allows the case identification to implemented in association with any routine data source which has clinically coded data.

The tool kit consists of the following elements:

The pregnancy ontology has been developed using the OWL (Web Ontology Language) using the Protege ontology development environment.

The pregnancy ontology can be graphically browsed online at:
NCBO ontology browser (BioPortal): https://bioportal.bioontology.org/ontologies/PREGONTO

Case identification process steps

  • Mapping clinical coding terminology to ontology terms

    The terms in the pregnancy ontology is annotated using codes from the target clinical coding system (Code mappings for UK Read ver2 and CTV3 are currently available).

  • Prepare input tables from data source

    The input tables (common format) are created using relevant data extracted from the data source.

  • Execute case finding algorithm

    The pregnancy case finding algorithm is execute on the input tables.

  • Analyse output data

    The output tables are produced by the algorithm. Output tables include 1) Pregnancy episode table for each of the pregnancies in the input table 2) Pregnancy event table with all ontology related events/codes for each of the pregnancies identified by the algorithm.

Pregnancies identified can be based on recorded events or calculated events (in the absence of start and stop pregnancy indicators) as shown in the figure below.

Related publications

  • Liyanage H, Williams J, Byford R, Stergioulas L, de Lusignan. Ontologies in big health data analytics: application to routine clinical data (Accepted for EFMI STC 2018)

Pregnancy ontology development team

  • Harshana Liyanage, John Williams, Rachel Byford, Simon de Lusignan