Modeling Medical Guidelines by Prova and SHACL Accessing FHIR/RDF. Use Case: The Medical ABCDE Approach

Abstract

Decision-making based on so-called medical guidelines supported by semantic AI solutions is an essential and significant task for medical personnel in both a pre-clinical setting and an inner-clinical environment. Semantic representations of medical guidelines and Fast Healthcare Interoperability Resources (FHIR) using Semantic Web technologies, i.e., Resource Description Framework (RDF), rules (RuleML and Prova), and Shape Constraint Language (SHACL), provide a semantic knowledge base for the decision-making process and ease technical implementation and automation tasks. Current medical decision support systems lack Semantic Web integration using FHIR-RDF representations as a data source. In this paper, we implement a particular medical guideline using two different approaches: Prova [8] and SHACL [13]. We generate a series of raw FHIR-data for a selected guideline, the ABCDE approach, and compare the implemented two programs’ (Prova and SHACL) results. Both approaches deliver the same results in terms of content. Both may be used within a distributed medical environment depending on the need of organizations.