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Electronic Health Records are clinical information repositories that have been proposed primarily to provide access to all clinical data of a patient. They have been formally defined by a dual model composed of a reference model and an archetype model. Such dual approach allows semantic interoperability, thus making different systems understand each other. In this work we extend the current structure with a third Decisional Model that will allow reasoning over the embedded clinical contents. Such reasoning will be based on the reuse of the clinical experience gained by the corresponding clinical professionals during different decision procedures. [ABSTRACT FROM PUBLISHER]

Highlights: [•] We identify the most notorious reported shortcomings in CDSS. [•] We formulate a clinical task model (CTM). [•] We identify the role of CDSS in the CTM. [•] We provide a generic architecture for continuous learning of CDSS from experience. [•] We present an implementation of the system in the breast cancer domain. [ABSTRACT FROM AUTHOR]

Ontology processing is arguably a time-consuming process with high associated computational costs. Query actions constitute a crucial part of the reasoning process and are a primary source of time consumption. Reflexive ontologies (ROs) is a novel approach intended to reduce time consumption problems while providing a fast reaction from ontology-based applications. In this article we present the implementation of a knowledge-based clinical decision support system (CDSS) for the diagnosis of Alzheimer's disease, which was the benchmark used to evaluate the impact of RO in the overall performance of the system. The implementation details and the definition of the implementation methodology are exposed in this article, along with the results of the evaluation. Some novel techniques that aim to optimize the performance of ROs are also presented with highlights of the test application introduced in our previous work. [ABSTRACT FROM AUTHOR]