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On the Feasibility of Distributed Process Mining in Healthcare

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On the Feasibility of Distributed Process Mining in Healthcare

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dc.contributor.author Gatta, Roberto es_ES
dc.contributor.author Vallati, Mauro es_ES
dc.contributor.author Lenkowicz, Jacopo es_ES
dc.contributor.author Masciocchi, Carlota es_ES
dc.contributor.author Cellini, Francesco es_ES
dc.contributor.author Boldrini, Luca es_ES
dc.contributor.author Fernández Llatas, Carlos es_ES
dc.contributor.author Valentini, Vincenzo es_ES
dc.contributor.author Damiani, Andrea es_ES
dc.date.accessioned 2022-02-10T08:42:47Z
dc.date.available 2022-02-10T08:42:47Z
dc.date.issued 2019-06-14 es_ES
dc.identifier.isbn 978-3-030-22734-0 es_ES
dc.identifier.issn 0302-9743 es_ES
dc.identifier.uri http://hdl.handle.net/10251/180678
dc.description.abstract [EN] Process mining is gaining significant importance in the healthcare domain, where the quality of services depends on the suitable and efficient execution of processes. A pivotal challenge for the application of process mining in the healthcare domain comes from the growing importance of multi-centric studies, where privacy-preserving techniques are strongly needed. In this paper, building on top of the well-known Alpha algorithm, we introduce a distributed process mining approach, that allows to overcome problems related to privacy and data being spread around. The introduced technique allows to perform process mining without sharing any patients-related information, thus ensuring privacy and maximizing the possibility of cooperation among hospitals. es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof Computational Science - ICCS 2019. Lecture Notes in Computer Science es_ES
dc.relation.ispartofseries Lecture Notes in Computer Science;11540 es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Process mining es_ES
dc.subject Healthcare es_ES
dc.subject Distributed learning es_ES
dc.title On the Feasibility of Distributed Process Mining in Healthcare es_ES
dc.type Comunicación en congreso es_ES
dc.type Artículo es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-030-22750-0_36 es_ES
dc.rights.accessRights Cerrado es_ES
dc.description.bibliographicCitation Gatta, R.; Vallati, M.; Lenkowicz, J.; Masciocchi, C.; Cellini, F.; Boldrini, L.; Fernández Llatas, C.... (2019). On the Feasibility of Distributed Process Mining in Healthcare. Springer. 445-452. https://doi.org/10.1007/978-3-030-22750-0_36 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename International Conference on Computational Science (ICCS 2019) es_ES
dc.relation.conferencedate Junio 12-14,2019 es_ES
dc.relation.conferenceplace Faro, Portugal es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-22750-0_36 es_ES
dc.description.upvformatpinicio 445 es_ES
dc.description.upvformatpfin 452 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\409667 es_ES
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