The logic of surveillance guidelines: an analysis of vaccine adverse event reports from an ontological perspective

• Published in: PloS one 2014-03, Vol.9 (3), p.e92632
• Main Authors: Courtot, Mélanie, Brinkman, Ryan R, Ruttenberg, Alan
• Format: Article
• Language: English
• Subjects: Analysis; Anaphylaxis; Asthma; Automation; Biology and Life Sciences; Coding; Collaboration; Complications and side effects; Computer and Information Sciences; Datasets as Topic; Diagnosis; Diagnosis, Computer-Assisted - methods; Female; Guideline Adherence; Guidelines; Guidelines as Topic; Humans; Immunization; Independent regulatory commissions; Influenza; Influenza A Virus, H1N1 Subtype; Influenza Vaccines - administration & dosage; Influenza Vaccines - adverse effects; Knowledge representation; Licenses; Male; Medical screening; Medicine and Health Sciences; Models, Biological; Ontology; Pharmacology; Pharmacovigilance; Predictive Value of Tests; Product safety; Public health; Regulatory agencies; Research and Analysis Methods; Semantic web; Surveillance; Surveillance equipment; Surveillance systems; Vaccines; Web Ontology Language-OWL
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0092632

When increased rates of adverse events following immunization are detected, regulatory action can be taken by public health agencies. However to be interpreted reports of adverse events must be encoded in a consistent way. Regulatory agencies rely on guidelines to help determine the diagnosis of the adverse events. Manual application of these guidelines is expensive, time consuming, and open to logical errors. Representing these guidelines in a format amenable to automated processing can make this process more efficient. Using the Brighton anaphylaxis case definition, we show that existing clinical guidelines used as standards in pharmacovigilance can be logically encoded using a formal representation such as the Adverse Event Reporting Ontology we developed. We validated the classification of vaccine adverse event reports using the ontology against existing rule-based systems and a manually curated subset of the Vaccine Adverse Event Reporting System. However, we encountered a number of critical issues in the formulation and application of the clinical guidelines. We report these issues and the steps being taken to address them in current surveillance systems, and in the terminological standards in use. By standardizing and improving the reporting process, we were able to automate diagnosis confirmation. By allowing medical experts to prioritize reports such a system can accelerate the identification of adverse reactions to vaccines and the response of regulatory agencies. This approach of combining ontology and semantic technologies can be used to improve other areas of vaccine adverse event reports analysis and should inform both the design of clinical guidelines and how they are used in the future. Sufficient material to reproduce our results is available, including documentation, ontology, code and datasets, at http://purl.obolibrary.org/obo/aero.