Using TimeML to Support the Modeling of Computerized Clinical Guidelines

The modeling of clinical guidelines in order to apply them in computerized medical tools is a challenging and laborious task. In this project we show that conditional subordination links--a temporal relation concept of TimeML--can be used to describe condition-based activities in a guideline. Therefore, we extend the specification of TimeML concerning events and subordination links. Subsequently, linguistic and semantic rules are developed to automatically generate annotations for these links and classify them as relevant for the clinical care path. Finally, the evaluation of the method shows that this categorization supports the task of the guideline modeling expert.