Knowledge representation in Health Research: the modeling of Adverse Events Following Immunization

Free-text reporting of Adverse Events Following Immunization (AEFIs) leads to inaccurate and incomplete data. Accurate representation of adverse event is a crucial part of clinical research: it may initiate further investigation of potential problems in vaccine safety or efficacy, and facilitate subsequent dissemination of safety-related information to the scientific community and the public [1,2]. However, current methods used for adverse events reporting are not sufficient, mitigating their usefulness. There is no standardization of the terminology used in the current Electronic Data Capture System used by Public Health Agency of Canada – at best a Medical Dictionary of Regulatory Activities (MedDRA [3]) code is assigned after parsing the clinician’s input, but this code is not linked to any definition. Several studies highlight the potential issues in using MedDRA for adverse event reporting, ranging from inaccurate reporting (as several terms are non-exact synonyms) to lack of semantic grouping features impairing processing in pharmacovigilance [4-8]. Additionally, only the final adverse event code as determined by the system is saved, and information about sub-parts are lost, therefore restricting ability of the physician to go back to the set of symptoms observed to establish the diagnostic, and limiting the ability to query the resulting datasets.