Diagnostic criteria and clinical guidelines standardization to automate case classification

When increased rates of adverse events following immunization are reported, regulatory action can be taken by public health agencies charged with protecting the safety of the public. However, to even be interpreted, reports of adverse events must be encoded in a consistent way. Regulatory agencies rely on guidelines to help determine such encoding. Their application, if done manually, is expensive and time consuming. In the interest of making this process more efficient it is desirable to represent these guidelines in a format amenable to automated processing. This paper describes the Adverse Event Reporting Ontology (AERO), which provides a representation of guidelines classifying adverse events. The representation is novel in that it supports a balance between the ability to express different organizations guidelines and the need to use common terminology wherever possible. Having developed the framework with immunization related adverse events in mind, we show that it is nonetheless flexible enough to be applied to a different domain malaria diagnosis. We also demonstrate how OWL reasoning can be leveraged to help compute such assessments and discuss prospects and hopes for adoption of the approach.