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dc.contributor.authorCanensi, Luca
dc.contributor.authorLeonardi, Giorgio
dc.contributor.authorMontani, Stefania
dc.contributor.authorTerenziani, Paolo
dc.date.accessioned2019-08-20T00:53:15Z
dc.date.available2019-08-20T00:53:15Z
dc.date.issued2018
dc.identifier.issn1826-6223
dc.identifier.doi10.20368/1971-8829/1453
dc.identifier.urihttp://hdl.handle.net/10072/384750
dc.description.abstractMedical process mining is gaining much attention in recent years, but the available mining algorithms can hardly cope with medical application peculiarities, that require to properly contextualize process patterns. Indeed, most approaches lose the connection between a mined pattern and the relevant portion of the input event log, and can have a limited precision, i.e., they can mine incorrect paths, never appearing in the input log traces. These issues can be very harmful in medical applications, where it is vital that mining results are reliable as much as possible, and properly reference the contextual information, in order to facilitate the work of physicians and hospital managers in guaranteeing the highest quality of service to patients. In this paper, we propose a novel approach to medical process mining that operates in a context-aware fashion. We show on a set of critical examples how our algorithm is able to cope with all the issues sketched above. In the future, we plan to test the approach on a real-world medical dataset, and to extend the framework in order to support ef cient and exible trace querying as well.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherItalian e-Learning Association
dc.publisher.urihttp://www.je-lks.org/ojs/index.php/Je-LKS_EN/article/view/1453
dc.relation.ispartofpagefrom33
dc.relation.ispartofpageto44
dc.relation.ispartofissue1
dc.relation.ispartofjournalJe-LKS: Journal of E-Learning and Knowledge Society
dc.relation.ispartofvolume14
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchSpecialist Studies in Education
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode1303
dc.titleA Context-Aware Miner for Medical Processes
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/3.0
dc.description.versionPublished
gro.rights.copyright© The Author(s) 2018. This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorTerenziani, Paolo


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