  The automated capturing and summarization of medical consultations has the
potential to reduce the administrative burden in healthcare. Consultations are
structured conversations that broadly follow a guideline with a systematic
examination of predefined observations and symptoms to diagnose and treat
well-defined medical conditions. A key component in automated conversation
summarization is the matching of the knowledge graph of the consultation
transcript with a medical domain ontology for the interpretation of the
consultation conversation. Existing general medical ontologies such as SNOMED
CT provide a taxonomic view on the terminology, but they do not capture the
essence of the guidelines that define consultations. As part of our research on
medical conversation summarization, this paper puts forward a semi-automated
method for generating an ontological representation of a medical guideline. The
method, which takes as input the well-known SNOMED CT nomenclature and a
medical guideline, maps the guidelines to a so-called Medical Guideline
Ontology (MGO), a machine-processable version of the guideline that can be used
for interpreting the conversation during a consultation. We illustrate our
approach by discussing the creation of an MGO of the medical condition of ear
canal inflammation (Otitis Externa) given the corresponding guideline from a
Dutch medical authority.
