Description
Computational representations of the semantic knowledge embedded within clinical practice guidelines (CPGs) may be a significant aid in creating computer interpretable guidelines (CIGs). Formalizing plain text CPGs into CIGs manually is a laborious and burdensome task, even using CIG tools and languages designed to improve the process. Natural language understanding (NLU) systems perform automated reading comprehension, parsing text and using reasoning to convert syntactic information from unstructured text into semantic information. Influenced by successful systems used in other domains, we present the architecture for a system which uses NLU approaches to create semantic representations of entire CPGs. In the future, these representations may be used to generate CIGs.
Learning Objective: After participating in this session, the learner should understand the state of the art in, and problems with, CPG understanding systems. They should learn about the challenges in building natural language understanding systems for guidelines and a possible solution to the problems.
Authors:
Daniel Schlegel (Presenter)
SUNY Oswego
Kate Gordon, SUNY Oswego
Carmelo Gaudioso, Roswell Park Cancer Institute
Mor Peleg, University of Haifa
Presentation Materials: