Integrating Document-Based and Knowledge-Based Models for Clinical Guidelines Analysis

摘要

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. We illustrate this through a case study developed around French guidelines for the management of hypertension. Recommendations included in the textual guideline provide complementary information for the instantiation of an HTN on specific patient data. The mapping takes place at different levels, from the pre-condition of operators to the rules playing a role as selection heuristics when extracting a solution sub-graph. Such a process, which explores the textual document from the prospective of a task model, can help analyzing the overall structure of clinical guidelines and ultimately improving its applicability.