title: Ontologies to capture adverse events following immunisation (AEFI) from real world health data.
abstract: Immunisation is an important part of health care and adverse events following immunisation (AEFI) are relatively rare. AEFI can be detected through long term follow up of a cohort or from looking for signals from real world, routine data; from different health systems using a variety of clinical coding systems. Mapping these is a challenging aspect of integrating data across borders. Ontological representations of clinical concepts provide a method to map similar concepts, in this case AEFI across different coding systems. We describe a method using ontologies to be flag definite, probable or possible cases. We use Guillain-Barre syndrome (GBS) as an AEFI to illustrate this method, and the Brighton collaboration's case definition of GBS as the gold standard. Our method can be used to flag definite, probable or possible cases of GBS. Whilst there has been much research into the use of ontologies in immunisation these have focussed on database interrogation; where ours looks to identify varying signal strength.
authors.affiliation: Department of Health Care Management & Policy, University of Surrey, UK.
authors.forename: Harshana
authors.initials: H
authors.lastname: Liyanage
authors.affiliation: Department of Health Care Management & Policy, University of Surrey, UK.
authors.forename: Simon
authors.initials: S
authors.lastname: de Lusignan
mesh_terms: D064229:Biological Ontologies; D064420:Drug-Related Side Effects and Adverse Reactions / Q000145:classification* / Q000209:etiology*; D057286:Electronic Health Records / Q000145:classification*; D020275:Guillain-Barre Syndrome / Q000145:classification* / Q000517:prevention & control*; D007114:Immunization / Q000009:adverse effects* / Q000145:classification*; D009323:Natural Language Processing