Abstract
Semantic interoperability represents one of the main challenges in health information systems. The development of novel interoperability models should promote the integration of heterogeneous information in the acquisition and semantic analysis of complex data patterns, which are typically used in clinical information. The purpose of this study is to develop a knowledge-based decision support system that uses ontologies for integrating data related to hypertensive disorders in pregnancy. This model allows, when dealing with new cases, inferring from a knowledge base and predicting high-risk situations that could lead to serious problems during gestation in both pregnant women and fetuses. Results demonstrate that the use of ontologies to address semantically acquired patterns from different electronic health records has the potential to significantly influence a service-oriented architecture implementation for clinical decision support systems.
•A comparative analysis of standards used as pattern reference model for CDSSs.•OpenEHR Performance and its compatibility keeping semantics interoperability of HISs.•Archetypes to support the identification of high-risk situations in pregnancy.•Proposed semantic model performance of a SOA for gestation related chronic diseases.