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dc.contributor.authorGarcía-Peñalvo, Francisco
dc.contributor.authorVázquez-Ingelmo, Andrea
dc.contributor.authorGarcía-Holgado, Alicia
dc.contributor.authorSampedro-Gómez, Jesús
dc.contributor.authorSánchez-Puente, Antonio
dc.contributor.authorVicente-Palacios, Víctor
dc.contributor.authorDorado-Díaz, P. Ignacio
dc.contributor.authorSánchez, Pedro L.
dc.date2023-01
dc.date.accessioned2023-03-09T15:55:31Z
dc.date.available2023-03-09T15:55:31Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/14314
dc.description.abstractMachine Learning (ML) has extended its use in several domains to support complex analyses of data. The medical field, in which significant quantities of data are continuously generated, is one of the domains that can benefit from the application of ML pipelines to solve specific problems such as diagnosis, classification, disease detection, segmentation, assessment of organ functions, etc. However, while health professionals are experts in their domain, they can lack programming and theoretical skills regarding ML applications. Therefore, it is necessary to train health professionals in using these paradigms to get the most out of the application of ML algorithms to their data. In this work, we present a platform to assist non-expert users in defining ML pipelines in the health domain. The system’s design focuses on providing an educational experience to understand how ML algorithms work and how to interpret their outcomes and on fostering a flexible architecture to allow the evolution of the available components, algorithms, and heuristics.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;In Press
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/3249es_ES
dc.rightsopenAccesses_ES
dc.subjectartificial intelligencees_ES
dc.subjecthuman-computer interaction (HCI)es_ES
dc.subjecthealthes_ES
dc.subjectinformation systemes_ES
dc.subjectmedical dataes_ES
dc.subjectmedical imageses_ES
dc.subjectIJIMAIes_ES
dc.titleKoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionalses_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2023.01.006


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