KoopaML, a Machine Learning platform for medical data analysis

• Autor(a) principal: García-Holgado, Alicia
• Data de Publicação: 2022
• Outros Autores: Vázquez-Ingelmo, Andrea, Alonso-Sánchez, Julia, García-Peñalvo, Francisco José, Therón, Roberto, Sampedro-Gómez, Jesús, Sánchez-Puente, Antonio, Vicente-Palacios, Víctor, Dorado-Díaz, P. Ignacio, Sánchez, Pedro L.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Journal on Interactive Systems
• Texto Completo: https://sol.sbc.org.br/journals/index.php/jis/article/view/2574
• topic: Machine Learning; Data Analysis; Machine Learning Pipelines; Learning Platform; Health

Machine Learning allows facing complex tasks related to data analysis with big datasets. This Artificial Intelligence branch allows not technical contexts to get benefits related to data processing and analysis. In particular, in medicine, medical professionals are increasingly interested in Machine Learning to identify patterns in clinical cases and make predictions regarding health issues. However, many do not have the necessary programming or technological skills to perform these tasks. Many different tools focus on developing Machine Learning pipelines, from libraries for developers and data scientists to visual tools for experts or platforms to learn. However, we have identified some requirements in the medical context that raise the need to create a customized platform adapted to end-user found in this context. This work describes the design process and the first version of KoopaML, an ML platform to bridge the data science gaps of physicians while automatizing Machine Learning pipelines. The platform is focused on enhanced interactivity to improve the engagement of physicians while still providing all the benefits derived from the introduction of Machine Learning pipelines in medical departments, as well as integrated ongoing training during the use of the tool’s features.