Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems. Issue 123 (January 2016) - Zhang, Yi-Fan
Highlights: We develop a semantic healthcare knowledge base to support the practical use of CDSS. We propose a unified representation of healthcare domain knowledge and patient data based on HL7 RIM and ontology. We encode semantic rules and queries for data-driven and knowledge-based inference. We design a semantic CDSS to enable data interoperability and knowledge sharing for patient-specific clinical decision support. Abstract: Background and objectives: The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism. This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications. Methods: A four-phase knowledge engineering cycle is implemented to develop a semantic healthcare knowledge base based on an HL7 reference information model, including an ontology to model domain knowledge and patient data and an expression repository to encode clinical decision making rules and queries. A semantic clinical decision support system is designed to provide patient-specific healthcare recommendations based on the knowledge base and patient data. Results: The proposed solution is evaluated in the case study of type 2 diabetes mellitus inpatient management. The knowledge base is successfully instantiated with relevant domain knowledge and testing patient data. Ontology-level evaluation confirms model validity. Application-level evaluation of diagnostic accuracy reaches a sensitivity of 97.5%, a specificity of 100%, and a precision of 98%; an acceptance rate of 97.3% is given by domain experts for the recommended care plan orders. Conclusions: The proposed solution has been successfully validated in the case study as providing clinical decision support at a high accuracy and acceptance rate. The evaluation results demonstrate the technical feasibility and application prospect of our approach.