Integrating Document-based and Knowledge-based Models for Clinical Guideline Analysis

Abstract. Research in the computerization of Clinical Guidelines (CG) has often opposed document-based approaches to knowledge-based ones. In this paper, we suggest that both approaches can be used simultaneously to assess the contents of textual Clinical Guidelines. In this first experiment, we investigate the mapping between a document model, which has been marked-up to structure its recommendations, and a knowledge structure representing the management of specific disease. This knowledge representation is based on planning formalisms, more specifically Hierarchical Task Networks (HTN). Our system operates by first automatically encoding the textual guideline through the identification of specific expressions with surface natural language processing, as described in previous work. In a subsequent step, the HTN, constructed manually and independently, and represented as an explicit AND/OR graph, is searched for a solution sub-graph using an algorithm derived from AO*. Whilst the HTN is being traversed, corresponding information is accessed in the encoded textual CG, to guide the solution extraction process