Where has been this library used?
This library has been used in the following projects and scientific papers so far:
Projects
ASTUTENESS: AI-Driven Tools In Healthcare: A Visual Guideline for Trustworthy Treatment Decision Support Systems (grant agreement No 101035821): supported by the European University for Well-being - Research (EUniWell - Research) and by the European Union’s Horizon 2020 research and innovation programme.
GRALENIA (Ref. 2021/C005/00150055): supported by the Spanish Ministry of Economic Affairs and Digital Transformation, the Spanish Secretariat of State for Digitization and Artificial Intelligence, Red.es and by the NextGenerationEU funding.
CONFAINCE (Ref. PID2021-122194OB-I00): supported by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, by the “European Union”.
Scientific papers
Antonio Lopez-Martinez-Carrasco, Jose Juarez, Manuel Campos, and Bernardo Canovas-Segura. “A Methodology Based on Subgroup Discovery to Generate Reduced Subgroup Sets for Patient Phenotyping”. In: Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2, 2024, pp. 346-353. DOI: http://dx.doi.org/10.5220/0012321200003657
Antonio Lopez-Martinez-Carrasco, Hugo Manuel Proença, Jose M. Juarez, Matthijs van Leeuwen, and Manuel Campos. “Novel approach for phenotyping based on diverse top-k subgroup lists”. In: 21st International Conference on Artificial Intelligence in Medicine, 2023, pp. 45-50. DOI: https://doi.org/10.1007/978-3-031-34344-5_6
Antonio Lopez-Martinez-Carrasco, Hugo Manuel Proença, Jose M. Juarez, Matthijs van Leeuwen, and Manuel Campos. “Discovering Diverse Top-K Characteristic Lists”. In: Advances in Intelligent Data Analysis XXI - 21st International Symposium on Intelligent Data Analysis, 2023, pp. 262-273. DOI: https://doi.org/10.1007/978-3-031-30047-9_21
Antonio Lopez-Martinez-Carrasco, Jose M. Juarez, Manuel Campos, and Bernardo Canovas-Segura. “VLSD - An efficient Subgroup Discovery algorithm based on Equivalence Classes and Optimistic Estimate”. In: Algorithms, 16 (2023), p. 274. DOI: https://doi.org/10.3390/a16060274