Discovering multiple antibiotic resistance phenotypes using diverse top-k subgroup list discovery

Artificial Intelligence in Medicine, vol. 167, pp. 103200

Abstract

Antibiotic resistance is one of the major global threats to human health and occurs when antibiotics lose their ability to combat bacterial infections. In this problem, a clinical decision support system could use phenotypes in order to alert clinicians of the emergence of patterns of antibiotic resistance in patients. Patient phenotyping is the task of finding a set of patient characteristics related to a specific medical problem such as the one described in this work. However, a single explanation of a medical phenomenon might be useless in the eyes of a clinical expert and be discarded. The discovery of multiple patient phenotypes for the same medical phenomenon would be useful in such cases. Therefore, in this work, we define the problem of mining diverse top-k phenotypes and propose the EDSLM algorithm, which is based on the Subgroup Discovery technique, the subgroup list model, and the Minimum Description Length principle. Our proposal provides clinicians with a method with which to obtain multiple and diverse phenotypes of a set of patients. We show a real use case of phenotyping in antimicrobial resistance using the well-known MIMIC-III dataset.

Authors 5

1. Antonio Lopez-Martinez-Carrasco corresponding

   Universidad de Murcia

   Affiliation as printed

   Med AI Lab, University of Murcia, Spain. Electronic address: antoniolopezmc@um.es

2. Hugo Manuel Proença Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands

3. José M. Juárez

   Universidad de Murcia

   Affiliation as printed

   Med AI Lab, University of Murcia, Spain

4. Matthijs van Leeuwen Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands

5. Manuel Campos

   Instituto Murciano de Investigación Biosanitaria · Universidad de Murcia

   Affiliation as printed

   Med AI Lab, University of Murcia, Spain; Murcian Bio-Health Institute (IMIB-Arrixaca), Spain