Ontology Model for Supporting Process Mining on Healthcare-Related Data
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https://doi.org/10.1007/978-3-031-34344-5_42 |
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Título
Ontology Model for Supporting Process Mining on Healthcare-Related DataAutoría
Fecha de publicación
2023-06-05Editor
SpringerISBN
978-3-031-34343-8; 978-3-031-34344-5Cita bibliográfica
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_42Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Lecture Notes in Computer Science (LNAI,volume 13897)Entidad financiadora
MCIN/AEI/10.13039/501100011033
Código del proyecto o subvención
PID2020-113723RB-C21 | PID2020-113723RB-C22 | RTI2018-099039-J-I00 | RYC2020-030190-I
Derechos de acceso
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
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