Phenotypes for Resistant Bacteria Infections Using an Efficient Subgroup Discovery Algorithm

摘要

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.