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.
摘要：
基于语义AI解决方案支持的所谓医疗指南的决策对于临床前环境和内部临床环境中的医务人员来说都是一项重要而重要的任务。使用语义Web技术的医疗指南和快速医疗保健互操作性资源（FHIR）的语义表示，即，资源描述框架(RDF)规则（RuleML和Prova），和形状约束语言(SHACL)，为决策过程提供语义知识库，简化技术实现和自动化任务。当前的医疗决策支持系统缺乏使用FHIR-RDF表示作为数据源的语义Web集成。在本文中,我们使用两种不同的方法实施特定的医学指南:Prova[8]和SHACL[13].我们为选定的指南生成一系列原始FHIR数据，ABCDE方法，并比较实施的两个程序（Prova和SHACL）的结果。两种方法在内容方面提供相同的结果。根据组织的需要，两者都可以在分布式医疗环境中使用。
