Semantic Annotations in Clinical Guidelines

Clinical guidelines are evidence-based recommendations developed to assist practitioners in their decisions on appropriate care for patients with specific clinical circumstances. They provide succinct instructions such as what drugs should be given or taken a particular condition, how long treatment given, tests conducted, other situational circumstances certain diseases. However, they described natural language, prone problems variability and ambiguity. In this paper, we propose an approach automatically infer the main components guideline sentences. Knowing key concepts sentences, can then feed them model checkers validate correctness. We adapt semantic role labelling mark entities our problem domain. also implement technique used Named-Entity Recognition (NER) task compare results. The aim of work is build reasoning framework that combines information gained from real patient data practice, give more suitable personalised treating patients.