2026-09-15T04:52:21Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1538282025-12-19T09:04:00Zcom_10366_4549com_10366_4512com_10366_3823col_10366_4550

Vázquez Ingelmo, Andrea García Holgado, Alicia García Peñalvo, Francisco J. Andrés-Fraile, Esther Pérez-Sánchez, Pablo Antúnez-Muiños, Pablo Sánchez-Puente, Antonio Vicente-Palacios, Víctor Dorado Díaz, Pedro Ignacio Cruz González, Ignacio Sánchez Fernández, Pedro Luis 2023-12-05T19:19:20Z 2023-12-05T19:19:20Z 2023 0302-9743 http://hdl.handle.net/10366/153828 10.1007/978-3-031-33023-0_30 1611-3349 The use of Machine Learning (ML) to resolve complex tasks has become popular in several contexts. While these approaches are very effective and have many related benefits, they are still very tricky for the general audience. In this sense, expert knowledge is crucial to apply ML algorithms properly and to avoid potential issues. However, in some situations, it is not possible to rely on experts to guide the development of ML pipelines. To tackle this issue, we present an approach to provide customized heuristics and recommendations through a graphical platform to build ML pipelines, namely KoopaML, focused on the medical domain.With this approach, we aim not only at providing an easy way to apply ML for non-expert users, but also at providing a learning experience for them to understand how these methods work. application/pdf eng Atribución-NoComercial-CompartirIgual 4.0 Internacional http://creativecommons.org/licenses/by-nc-sa/4.0/ info:eu-repo/semantics/openAccess Information system Medical data management Medical imaging management Artificial Intelligence Health platform HCI 1203.17 Informática 1203.04 Inteligencia Artificial 3212 Salud Publica Flexible Heuristics for Supporting Recommendations Within an AI Platform Aimed at Non-expert Users info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion PID2020-118345RB-I00 13869 333 338