Representing
adverse events

Relevant resources

• Ontology for General Medical Science (OGMS)
• Information Artifact Ontology (IAO)
• Adverse Event Ontology (AEO)
• Adverse Event Reporting Ontology (AERO)

Reading material

Check out our zotero library or the list below:

• Kohl, K.S. et al. Applicability, reliability, sensitivity, and specificity of six Brighton Collaboration standardized case definitions for adverse events following immunization. Vaccine 26, 6349-6360 (2008).
• Gold, M.S., Gidudu, J., Erlewyn-Lajeunesse, M. & Law, B. Can the Brighton Collaboration case definitions be used to improve the quality of Adverse Event Following Immunization (AEFI) reporting? Anaphylaxis as a case study. Vaccine 28, 4487-4498 (2010).
• Kramer, M.S. & Lane, D.A. Causal propositions in clinical research and practice. Journal of Clinical Epidemiology 45, 639-649 (1992).
• Richesson, R.L., Fung, K.W. & Krischer, J.P. Heterogeneous but “standard” coding systems for adverse events: Issues in achieving interoperability between apples and oranges. Contemporary Clinical Trials 29, 635-645 (2008).
• Ceusters, W., Capolupo, M., de Moor, G., Devlies, J. & Smith, B. An evolutionary approach to realism-based adverse event representations. Methods Inf Med 50, 62-73 (2011).
• Bonhoeffer, J., Zumbrunn, B. & Heininger, U. Reporting of vaccine safety data in publications: systematic review. Pharmacoepidemiol Drug Saf 14, 101-106 (2005).
• Schulz, S. & Cornet, R. SNOMED CT’s Ontological Commitment. Nature Precedings (2009).doi:10.1038/npre.2009.3465.1
• Glymour, C. Statistics and Causal Inference: Comment: Statistics and Metaphysics. Journal of the American Statistical Association 81, 964-966 (1986).
• Ceusters, W.M., Spackman, K.A. & Smith, B. Would SNOMED CT benefit from Realism-Based Ontology Evolution? AMIA Annu Symp Proc 2007, 105-109 (2007).