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dc.contributor.authorNamatevs, Ivars
dc.contributor.authorAleksejeva, Ludmila
dc.date.accessioned2019-06-26T10:18:08Z
dc.date.available2019-06-26T10:18:08Z
dc.date.issued2017-06-01cs
dc.identifier.citationMendel. 2017 vol. 23, č. 1, s. 33-40. ISSN 1803-3814cs
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttp://hdl.handle.net/11012/179196
dc.description.abstractThis paper introduces the application of artificial intelligence paradigm towards precision medicine in renal transplantation. The match of the optimal donor-recipient pair in kidney transplantation in Latvian Transplant Centre (LTC) has been constrained by the lack of prediction models and algorithms. Consequently, LTC seeks for practical intelligent computing solution to assist the clinical setting decision-makers during their search for the optimal donor-recipient match. Therefore, by optimizing both the donor and recipient profiles, prioritizing importance of the features, and based on greedy algorithm approach, advanced decision algorithm has been created. The strength of proposed algorithm lies in identification of suitable donors for a specific recipient based on evaluation of criteria by points principle. Experimental study demonstrates that the decision algorithm for heuristic donor-recipient matching integrated in machine learning approach improves the ability of optimal allocation of renal in LTC. It is an important step towards personalized medicine in clinical settings.en
dc.formattextcs
dc.format.extent33-40cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/49cs
dc.subjectmachine learningen
dc.subjectgreedy algorithmen
dc.subjectevaluation criteria by pointsen
dc.subjectprecision medicineen
dc.titleDecision Algorithm for Heuristic Donor-Recipient Matchingen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
dc.coverage.issue1cs
dc.coverage.volume23cs
dc.identifier.doi10.13164/mendel.2017.1.033en
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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