Abstract
The discovery of phenotypes is useful to describe a population. Providing a set of diverse patient phenotypes with the same medical condition may help clinicians to understand it. In this paper, we approach this problem by defining the technical task of mining diverse top-k phenotypes and proposing an algorithm called DSLM to solve it. The phenotypes obtained are evaluated according to their quality and predictive capacity in a bacterial infection problem.
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Notes
- 1.
Available at subgroups python library or https://github.com/antoniolopezmc/subgroups.
References
Lavrac, N., Kavsek, B., Flach, P.A., Todorovski, L.: Subgroup discovery with CN2-SD. J. Mach. Learn. Res. 5, 153–188 (2004)
Lopez-Martinez-Carrasco, A., Juarez, J.M., Campos, M., Canovas-Segura, B.: VLSD - an efficient subgroup discovery algorithm based on equivalence classes and optimistic estimate. Knowledge and Information Systems. Status: submitted
Lopez-Martinez-Carrasco, A., Proença, H.M., Juarez, J.M., van Leeuwen, M., Campos, M.: Discovering diverse top-k characteristic lists. In: 21th Symposium on Intelligent Data Analysis (IDA 2023) (2023)
Proença, H.M., Grünwald, P., Bäck, T., van Leeuwen, M.: Robust subgroup discovery. Data Mining and Knowledge Discovery (2022)
Wojczynski, M.K., Tiwari, H.K.: Definition of phenotype. Genetic dissection of complex traits. Adv. Genetics 60, 75–105 (2008)
Acknowledgements
this work was partially funded by the CONFAINCE project (Ref: PID2021-122194OB-I00) by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union”, and by the GRALENIA project (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. This research was also partially funded by a national grant (Ref: FPU18/02220), of the Spanish Ministry of Science, Innovation and Universities (MCIU) and by a mobility grant (Ref: R-933/2021), of the University of Murcia.
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Lopez-Martinez-Carrasco, A., Proença, H.M., Juarez, J.M., Leeuwen, M.v., Campos, M. (2023). Novel Approach for Phenotyping Based on Diverse Top-K Subgroup Lists. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_6
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DOI: https://doi.org/10.1007/978-3-031-34344-5_6
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