Exploring Antimicrobial Resistance Prediction Using Post-hoc Interpretable Methods

An accurate and timely prediction of whether an infection is going to be resistant to a particular antibiotic could improve the clinical outcome of the patient as well as reduce the risk of spreading resistant microorganisms. From a data analysis perspective, four key factors are present in antimicrobial resistance prediction: the high dimensionality of the data available, the imbalance present in the datasets, the concept drift along time and the need for their acceptance and implantation by clinical staff. To date, no study has looked specifically at combining different strategies to deal with each of these four key factors. We believe interpretable prediction models are required. This study was undertaken to evaluate the impact of baseline interpretable predicting approaches using a dataset of real hospital data. In particular, we study the capacity of logistic regression, conditional trees and C5.0 rule-based models to improve the prediction when they are combined with oversampling, filtering and sliding windows.