A Python SDK for Authoring and using Computer-interpretable Guidelines.

In this paper we describe a Python SDK that we developed and which we used to create a decision support system (DSS) for determining and presenting clinical practice guideline (CPG) recommendations for individual patients. Computer-interpretable guidelines (CIGs) are formalisms that represent CPG knowledge. Our Python SDK implements a model and an engine for a CIG formalism that can be easily integrated into any Python-based application. We describe important aspects of creating a guideline model with our CIG and present a web application that interacts with our guideline engine through a REST API. The web application implements generic components to manage and display the current input needs, recommendations, and statements. In comparison to PROforma, we added predicate components, which facilitate the reuse of logical expressions. Arguments refer to predicates instead of including expressions. This allows reusing the same expression in multiple arguments. We also allow the use of t he predicates in other expressions, like in expressions of other predicates and task preconditions. To facilitate the integration of our CIG in decision support systems, we added properties to all PROforma components that represent a code from a terminology system.