TY - JOUR
KW - Artificial Intelligence
KW - Human-Computer Interaction (HCI)
KW - Health
KW - Information System
KW - Medical Data
KW - Medical Images
AU - Francisco García-Peñalvo
AU - Andrea Vázquez-Ingelmo
AU - Alicia García-Holgado
AU - Jesús Sampedro-Gómez
AU - Antonio Sánchez-Puente
AU - Víctor Vicente-Palacios
AU - P. Ignacio Dorado-Díaz
AU - Pedro L. Sánchez
AB - 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.
IS - In Press
M1 - In Press
N2 - 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.
PY - 9998
SE - 1
SP - 1
EP - 8
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - KoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals
UR - https://www.ijimai.org/journal/sites/default/files/2023-01/ip2023_01_006.pdf
VL - In Press
SN - 1989-1660
ER -