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Ontology Model for Supporting Process Mining on Healthcare-Related Data
dc.contributor.author | Miñarro Giménez, Jose Antonio | |
dc.contributor.author | Fernandez-Llatas, Carlos | |
dc.contributor.author | Martínez-Salvador, Begoña | |
dc.contributor.author | Martínez Costa, Catalina | |
dc.contributor.author | Marcos, Mar | |
dc.contributor.author | Fernández Breis, Jesualdo Tomás | |
dc.date.accessioned | 2024-04-25T09:01:27Z | |
dc.date.available | 2024-04-25T09:01:27Z | |
dc.date.issued | 2023-06-05 | |
dc.identifier.citation | Miñarro-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_42 | ca_CA |
dc.identifier.isbn | 978-3-031-34343-8 | |
dc.identifier.isbn | 978-3-031-34344-5 | |
dc.identifier.uri | http://hdl.handle.net/10234/206530 | |
dc.description.abstract | In 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.extent | 5 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer | ca_CA |
dc.relation.isPartOf | Lecture Notes in Computer Science (LNAI,volume 13897) | ca_CA |
dc.rights | © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | clinical process ontology | ca_CA |
dc.subject | electronic health record | ca_CA |
dc.subject | process mining | ca_CA |
dc.title | Ontology Model for Supporting Process Mining on Healthcare-Related Data | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1007/978-3-031-34344-5_42 | |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | MCIN/AEI/10.13039/501100011033 | ca_CA |
oaire.awardNumber | PID2020-113723RB-C21 | ca_CA |
oaire.awardNumber | PID2020-113723RB-C22 | ca_CA |
oaire.awardNumber | RTI2018-099039-J-I00 | ca_CA |
oaire.awardNumber | RYC2020-030190-I | ca_CA |
dc.subject.ods | 3. Salud y bienestar | ca_CA |
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