Abstract
Machine learning is increasingly present in different sectors. Decision-making processes that occur in all types of companies and entities can be improved with the use of AI algorithms and machine learning. Furthermore, the application of machine learning algorithms enables the possibility of providing support to automate the undertaking of complex tasks. However, not all users who want to use machine learning are skilled enough from a technological and data science point of view to use many of the tools that are already available on the market. In particular, the health sector is taking advantage of AI algorithms to enhance the decision-making processes and to support complex common activities. Nonetheless, physicians have the domain knowledge but are not deeply trained in data science. This is the case of the cardiology department of the University Hospital of Salamanca, where the large amount of anonymized data makes it possible to improve certain tasks and decision-making. This work describes a machine learning platform to assist non-expert users in the definition and application of ML pipelines. The platform aims to fill data science gaps while automatizing ML pipelines and provides a baseline to integrate it with other developed applications for the cardiology department.
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García-Holgado, A. et al. (2021). User-Centered Design Approach for a Machine Learning Platform for Medical Purpose. In: Ruiz, P.H., Agredo-Delgado, V., Kawamoto, A.L.S. (eds) Human-Computer Interaction. HCI-COLLAB 2021. Communications in Computer and Information Science, vol 1478. Springer, Cham. https://doi.org/10.1007/978-3-030-92325-9_18
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