Knowledge engineering: principles and methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Evaluating knowledge engineering techniques
International Journal of Human-Computer Studies
Modeling a Decision Support System to Prevent Adverse Drug Events
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Detecting adverse events for patient safety research: a review of current methodologies
Journal of Biomedical Informatics - Patient safety
Hospital Care Watch (HCW): An Ontology and Rule-Based Intelligent Patient Management Assistant
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Applying Semantic Web Technologies to Drug Safety Determination
IEEE Intelligent Systems
Artificial Intelligence in Medicine
International Journal of Intelligent Systems
A goal-oriented framework for specifying clinical guidelines and handling medical errors
Journal of Biomedical Informatics
Adverse events in medicine: Easy to count, complicated to understand, and complex to prevent
Journal of Biomedical Informatics
IEEE Transactions on Information Technology in Biomedicine
Artificial Intelligence in Medicine
Data Mining to Generate Adverse Drug Events Detection Rules
IEEE Transactions on Information Technology in Biomedicine
Methodological Review: Computer-interpretable clinical guidelines: A methodological review
Journal of Biomedical Informatics
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The primary aim of this work was the development of a uniform, contextualized and sustainable knowledge-based framework to support adverse drug event (ADE) prevention via Clinical Decision Support Systems (CDSSs). In this regard, the employed methodology involved first the systematic analysis and formalization of the knowledge sources elaborated in the scope of this work, through which an application-specific knowledge model has been defined. The entire framework architecture has been then specified and implemented by adopting Computer Interpretable Guidelines (CIGs) as the knowledge engineering formalism for its construction. The framework integrates diverse and dynamic knowledge sources in the form of rule-based ADE signals, all under a uniform Knowledge Base (KB) structure, according to the defined knowledge model. Equally important, it employs the means to contextualize the encapsulated knowledge, in order to provide appropriate support considering the specific local environment (hospital, medical department, language, etc.), as well as the mechanisms for knowledge querying, inference, sharing, and management. In this paper, we present thoroughly the establishment of the proposed knowledge framework by presenting the employed methodology and the results obtained as regards implementation, performance and validation aspects that highlight its applicability and virtue in medication safety.