Abstract
The phenotyping process consists of selecting sets of patients of special interest and identifying their key characteristics. Subgroup Discovery (SD) is a suitable supervised approach for this task. In this work, we have proposed a two step process with an efficient SD algorithm (VLA4SD) for an exhaustive exploration of the search space with very effective prunes based on equivalence classes. We use the Coverage and the Incremental Response Rate quality measures to evaluate general and interesting subgroups. The suitability of our approach has been tested by identifying phenotypes of patients in the MIMIC-III open access database.
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Acknowledgment
This work was partially funded by the SITSUS project (Ref: RTI2018-094832-B-I00), given by MCIU/AEI/FEDER, UE, and by a national grant (Ref:FPU18/02220) supported by the MCIU.
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Lopez-Martinez-Carrasco, A., Juarez, J.M., Campos, M., Canovas-Segura, B. (2021). Phenotypes for Resistant Bacteria Infections Using an Efficient Subgroup Discovery Algorithm. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_27
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DOI: https://doi.org/10.1007/978-3-030-77211-6_27
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