Recommendation Statements Identification in Clinical Practice Guidelines Using Heuristic Patterns

Clinical Practice Guidelines (CPGs) are considered as an effective tool to improve and standardize healthcare. A number of organizations are developing and maintaining clinical guidelines to provide state of the art healthcare services. However, the guidelines contain background information along with disease specific information which tends to create difficulties in using it during actual practice as well as in transforming it into a machine interpretable format. The most relevant information needs to be isolated from irrelevant information. In this study, we proposed a methodology that separates relevant information known as recommendation statements from irrelevant information by using heuristic patterns. We have extracted 10 patterns in a semi-automatic manner from hypertension guideline and evaluated the extracted patterns for identifying recommendation statements in the guideline and achieved 85.54% accuracy. These extracted patterns facilitate domain expert to get disease specific information in real time during the clinical workflow. Moreover, it can also work as a preprocessing step during the transformation of guideline to computer interpretable models.