Extending the GEM model to support knowledge extraction from textual guidelines

Clinical Practice Guidelines (CPGs) are being developed as a tool to promote best practice in medicine. However, the diffusion of paper guidelines has been shown to only have a limited impact. This is why computerization of CPGs has recently been suggested as a means to improve their dissemination as well as physicians' compliance. The Guideline Elements Model (GEM) has been proposed to facilitate the encoding of CPGs and support the automatic processing of marked-up documents. In this paper, we explore the automatic generation of a rule base from a textual guideline using GEM. In this study, we propose an extension of the GEM model that introduces additional levels of structuring centered on decision variables. This allows a more efficient representation of the decision processes, which supports the automatic generation of decision rules from textual guidelines. The 1999 Canadian recommendations for the management of hypertension have been marked-up as a GEM-encoded instance of our extended DTD. We derived a rule base using an XML parser to extract the relevant elements to instantiate the IF and THEN clauses of decision rules. The rule base automatically generated compares favourably with the manual generation of decision rules in the ASTI project. This approach is an interesting case study in the computerization of CPGs, as it illustrates processing steps that are relevant to the various aspects of CPGs life-cycle, from production to consultation and use.