Please use this identifier to cite or link to this item:
Title: Testing and Improvements of KoopaML: A Platform to Ease the Development of Machine Learning Pipelines in the Medical Domain
Authors: Vázquez-Ingelmo, A.
García-Holgado, A.
García-Peñalvo, F. J.
Fraile-Sanchón, R.
Pérez-Sánchez, P.
Antúnez-Muiños, P.
Sánchez-Puente, A.
Vicente-Palacios, V.
Dorado-Díaz, P. I.
Cruz-González, I.
Sánchez, P. L.
Keywords: Machine Learning
Human-Computer Interaction
Artificial Intelligence
Medical data management
Issue Date: 1-May-2023
Publisher: Springer
Abstract: Machine Learning (ML) applications in complex domains, such as the medical domain, can be highly beneficial, but also hazardous if some concepts are overlooked. In this context, however, health professionals denote expertise in their domain, but they often lack skills in terms of ML. In this sense, to leverage ML applications in the medical domain, it is important to combine both domain expertise and ML-related skills. In previous works, we tackled this challenge in the health context through a visual platform (KoopaML) that enables lay users to build ML pipelines. The present work describes the challenges derived from the first version of the platform and the prototypes for the new features designed to address them. The prototypes have been validated by two experts, obtaining highly valuable feedback.
Appears in Collections:Publications

Files in This Item:
File Description SizeFormat 
TEEM2022_KoopaML_post.pdfArticle1,11 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.