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dc.contributor.advisorBowles, Juliana
dc.contributor.authorRedeker, Guilherme Alfredo
dc.description.abstractBackground: Medication errors are a leading cause of preventable harm to patients. In older adults, the impact of ageing on the therapeutic effectiveness and safety of drugs is a significant concern, especially for those over 65. Consequently, certain medications called Potentially Inappropriate Medications (PIMs) can be dangerous in the elderly and should be avoided. Tackling PIMs by health professionals and patients can be time-consuming and error-prone, as the criteria underlying the definition of PIMs are complex and subject to frequent updates. Moreover, the criteria are not available in a representation that health systems can interpret and reason with directly. Objectives: This thesis aims to demonstrate the feasibility of using an ontology/rule-based approach in a clinical knowledge base to identify potentially inappropriate medication(PIM). In addition, how constraint solvers can be used effectively to suggest alternative medications and administration schedules to solve or minimise PIM undesirable side effects. Methodology: To address these objectives, we propose a novel integrated approach using formal rules to represent the PIMs criteria and inference engines to perform the reasoning presented in the context of a Clinical Decision Support System (CDSS). The approach aims to detect, solve, or minimise undesirable side-effects of PIMs through an ontology (knowledge base) and inference engines incorporating multiple reasoning approaches. Contributions: The main contribution lies in the framework to formalise PIMs, including the steps required to define guideline requisites to create inference rules to detect and propose alternative drugs to inappropriate medications. No formalisation of the selected guideline (Beers Criteria) can be found in the literature, and hence, this thesis provides a novel ontology for it. Moreover, our process of minimising undesirable side effects offers a novel approach that enhances and optimises the drug rescheduling process, providing a more accurate way to minimise the effect of drug interactions in clinical practice.en_US
dc.relationRedeker, G. A., & Kuster Filipe Bowles, J. (2023). An ontology-based approach for detecting and classifying inappropriate prescribing. In J. Vanthienen, T. Kliegr, P. Fodor, D. Lanti, D. Arndt, E. V. Kostylev, T. Mitsikas, & A. Soylu (Eds.), RuleML+RR’23: 17th International Rule Challenge and 7th Doctoral Consortium, September 18–20, 2023, Oslo, Norway (CEUR Workshop Proceedings; Vol. 3485). CEUR-WS. [ : Open Access version]en
dc.relationRedeker, G., & Kuster Filipe Bowles, J. (2020). Tackling polypharmacy: a multi-source decision support system. In L. Pape-Haugaard, C. Lovis, I. Corte Madsen, P. Weber, P. H. Nielsen, & P. Scott (Eds.), Digital Personalized Health and Medicine : Proceedings of MIE 2020 (Vol. 270, pp. 688 - 692). (Studies in Health Technology and Informatics; Vol. 270). IOS Press. [ : Open Access version]en
dc.subjectPotentially inappropriate medicationsen_US
dc.subjectInference rulesen_US
dc.subjectAlternative drugs recommendationen_US
dc.subjectDrug scheduling optimisationen_US
dc.subjectRule-based approachen_US
dc.subjectClinical decision support systemen_US
dc.titleA clinical decision support system for detecting and mitigating potentially inappropriate medicationsen_US
dc.contributor.sponsorUniversity of St Andrews. School of Computer Scienceen_US
dc.contributor.sponsorSoftware Competence Center Hagenberg (SCCH). INTEGRATEen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US
dc.identifier.grantnumberFFG grant no. 892418en_US

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