@article{3249,
  keywords = {Artificial Intelligence, Human-Computer Interaction (HCI), Health, Information System, Medical Data, Medical Images},
  author = {Francisco García-Peñalvo and Andrea Vázquez-Ingelmo and Alicia García-Holgado and Jesús Sampedro-Gómez and Antonio Sánchez-Puente and Víctor Vicente-Palacios and P. Ignacio Dorado-Díaz and Pedro L. Sánchez},
  title = {KoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals},
  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.},
  year = {9998},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {In Press},
  chapter = {1},
  number = {In Press},
  pages = {1-8},
  month = {01/2023},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2023-01/ip2023_01_006.pdf},
  doi = {10.9781/ijimai.2023.01.006},
}