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dc.contributor.authorGarcía Peñalvo, Francisco J. 
dc.contributor.authorVázquez Ingelmo, Andrea 
dc.contributor.authorGarcía Holgado, Alicia 
dc.date.accessioned2023-12-05T19:13:43Z
dc.date.available2023-12-05T19:13:43Z
dc.date.issued2022
dc.identifier.citationGarcía-Peñalvo, F. J., Vázquez-Ingelmo, A., & García-Holgado, A. (2022). Fostering Decision-Making Processes in Health Ecosystems Through Visual Analytics and Machine Learning. In P. Zaphiris & A. Ioannou (Eds.), Learning and Collaboration Technologies: Designing the Learner and Teacher Experience. 9th International Conference, LCT 2022, Held as Part of the 24th HCI International Conference, HCII 2022. Virtual Event, June 26 – July 1, 2022. Proceedings, Part II (pp. 262–273). Springer Nature. https://doi.org/10.1007/978-3-031-05675-8_20es_ES
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/10366/153826
dc.description.abstractData-intensive contexts, such as health, use information systems to merge, synthesize, represent, and visualize data by using interfaces to ease decision-making processes. All data management processes play an essential role in exploiting data’s strategic value from acquisition to visualization. Technological ecosystems allow the deployment of highly complex services while supporting their evolutionary nature. However, there is a challenge regarding the design of high-level interfaces that adapt to the evolving nature of data. The AVisSA project is focused on tackling the development of an automatic dashboard generation system (meta-dashboard) using Domain Engineering and Artificial Intelligence techniques. This approach makes it possible to obtain dashboards from data flows in technological ecosystems adapted to specific domains. The implementation of the meta-dashboard will make intensive use of user experience testing throughout its development, which will allowthe involvement of other actors in the ecosystem as stakeholders (public administration, health managers, etc.). These actors will be able to use the data for decision-making and design improvements in health provision.es_ES
dc.format.mimetypeapplication/pdf
dc.language.isoenges_ES
dc.rightsAtribución-NoComercial-CompartirIgual 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectDomain engineeringes_ES
dc.subjectSPLes_ES
dc.subjectMeta-modelinges_ES
dc.subjectInformation dashboardses_ES
dc.subjectInformation systemses_ES
dc.subjectHealthcarees_ES
dc.subjectHealth domaines_ES
dc.titleFostering Decision-Making Processes in Health Ecosystems Through Visual Analytics and Machine Learninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.unesco3212 Salud Publicaes_ES
dc.subject.unesco1203.17 Informáticaes_ES
dc.identifier.doi10.1007/978-3-031-05675-8_20
dc.relation.projectIDPID2020-118345RB-I00es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.essn1611-3349
dc.volume.number13329es_ES
dc.page.initial262es_ES
dc.page.final273es_ES
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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