KoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals.

Francisco José García Peñalvo; Andrea Vázquez Ingelmo; Alicia García Holgado; Jesús Sampedro Gómez; Antonio Sánchez Puente; Víctor Vicente Palacios; P. Ignacio Dorado Díaz; Pedro L. Sánchez

doi:10.9781/ijimai.2023.01.006

Authors

Francisco José García Peñalvo Universidad de Salamanca
Andrea Vázquez Ingelmo Universidad de Salamanca
Alicia García Holgado Universidad de Salamanca
Jesús Sampedro Gómez Universidad de Salamanca
Antonio Sánchez Puente Universidad de Salamanca
Víctor Vicente Palacios Philips Healthcare (Spain)
P. Ignacio Dorado Díaz Universidad de Salamanca
Pedro L. Sánchez Universidad de Salamanca

DOI:

https://doi.org/10.9781/ijimai.2023.01.006

Keywords:

Artificial Intelligence, Human-Computer Interaction (HCI), Health, Information System, Medical Data, Medical Images

Supporting Agencies

This research work has been supported by the Spanish Ministry of Education and Vocational Training under an FPU fellowship (FPU17/03276). This research was partially funded by the Spanish Government Ministry of Economy and Competitiveness through the DEFINES project grant number (TIN2016-80172-R) and the Ministry of Science and Innovation through the AVisSA project grant number (PID2020-118345RB-I00). This work was also supported by national (PI14/00695, PIE14/00066, PI17/00145, DTS19/00098, PI19/00658, PI19/00656 Institute of Health Carlos III, Spanish Ministry of Economy and Competitiveness and cofunded by ERDF/ESF, “Investing in your future”) and community (GRS 2033/A/19, GRS 2030/A/19, GRS 2031/A/19, GRS 2032/A/19, SACYL, Junta Castilla y León) competitive grants.

Abstract

Machine 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.

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