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dc.contributor.authorMiñarro Giménez, Jose Antonio
dc.contributor.authorFernandez-Llatas, Carlos
dc.contributor.authorMartínez-Salvador, Begoña
dc.contributor.authorMartínez Costa, Catalina
dc.contributor.authorMarcos, Mar
dc.contributor.authorFernández Breis, Jesualdo Tomás
dc.date.accessioned2024-04-25T09:01:27Z
dc.date.available2024-04-25T09:01:27Z
dc.date.issued2023-06-05
dc.identifier.citationMiñarro-Giménez, J.A., Fernández-Llatas, C., Martínez-Salvador, B., Martínez-Costa, C., Marcos, M., Fernández-Breis, J.T. (2023). Ontology Model for Supporting Process Mining on Healthcare-Related Data. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_42ca_CA
dc.identifier.isbn978-3-031-34343-8
dc.identifier.isbn978-3-031-34344-5
dc.identifier.urihttp://hdl.handle.net/10234/206530
dc.description.abstractIn the field of Medicine, Process Mining (PM) can be used to analyse healthcare-related data to infer the underlying diagnostic, treatment, and management processes. The PM paradigm provides techniques and tools to obtain information about the processes carried out by analysing the trace of healthcare events in the Electronic Health Records. In PM, workflows are the most frequent formalism used for representing the PM models. Despite the efforts to develop user-friendly tools, the understanding of PM models remains problematic. To improve this situation, we target the representation of PM models using ontologies. In this paper, we present a first version of the Clinical Process Model Ontology (CPMO), aimed at describing the sequential structure and associated metadata of PM models. Finally, we show the application of the CPMO to the domain of prostate cancer.ca_CA
dc.format.extent5 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringerca_CA
dc.relation.isPartOfLecture Notes in Computer Science (LNAI,volume 13897)ca_CA
dc.rights© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AGca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/ca_CA
dc.subjectclinical process ontologyca_CA
dc.subjectelectronic health recordca_CA
dc.subjectprocess miningca_CA
dc.titleOntology Model for Supporting Process Mining on Healthcare-Related Dataca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1007/978-3-031-34344-5_42
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA
project.funder.nameMCIN/AEI/10.13039/501100011033ca_CA
oaire.awardNumberPID2020-113723RB-C21ca_CA
oaire.awardNumberPID2020-113723RB-C22ca_CA
oaire.awardNumberRTI2018-099039-J-I00ca_CA
oaire.awardNumberRYC2020-030190-Ica_CA
dc.subject.ods3. Salud y bienestarca_CA


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