Grand challenges in clinical decision support
Journal of Biomedical Informatics
Towards the Merging of Multiple Clinical Protocols and Guidelines via Ontology-Driven Modeling
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
An Autonomous Algorithm for Generating and Merging Clinical Algorithms
Knowledge Management for Health Care Procedures
Home Care Personalisation with Individual Intervention Plans
Knowledge Management for Health Care Procedures
Automatic combination of formal intervention plans using SDA* representation model
AIME'07 Proceedings of the 2007 conference on Knowledge management for health care procedures
Authoring and verification of clinical guidelines: A model driven approach
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Metareasoning: Thinking about Thinking
Metareasoning: Thinking about Thinking
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Implementing an Integrative Multi-agent Clinical Decision Support System with Open Source Software
Journal of Medical Systems
Reasoning with effects of clinical guideline actions using OWL: AL amyloidosis as a case study
KR4HC'11 Proceedings of the 3rd international conference on Knowledge Representation for Health-Care
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
Knowledge-based verification of clinical guidelines by detection of anomalies
Artificial Intelligence in Medicine
Methodological Review: Computer-interpretable clinical guidelines: A methodological review
Journal of Biomedical Informatics
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We propose a new method to mitigate (identify and address) adverse interactions (drug-drug or drug-disease) that occur when a patient with comorbid diseases is managed according to two concurrently applied clinical practice guidelines (CPGs). A lack of methods to facilitate the concurrent application of CPGs severely limits their use in clinical practice and the development of such methods is one of the grand challenges for clinical decision support. The proposed method responds to this challenge. We introduce and formally define logical models of CPGs and other related concepts, and develop the mitigation algorithm that operates on these concepts. In the algorithm we combine domain knowledge encoded as interaction and revision operators using the constraint logic programming (CLP) paradigm. The operators characterize adverse interactions and describe revisions to logical models required to address these interactions, while CLP allows us to efficiently solve the logical models - a solution represents a feasible therapy that may be safely applied to a patient. The mitigation algorithm accepts two CPGs and available (likely incomplete) patient information. It reports whether mitigation has been successful or not, and on success it gives a feasible therapy and points at identified interactions (if any) together with the revisions that address them. Thus, we consider the mitigation algorithm as an alerting tool to support a physician in the concurrent application of CPGs that can be implemented as a component of a clinical decision support system. We illustrate our method in the context of two clinical scenarios involving a patient with duodenal ulcer who experiences an episode of transient ischemic attack.