A comparative study of fairness methods for clinical predictions using the MIMIC-IV database

Abstract

Fairness methods are an increasingly important aspect of responsible implementations of machine learning models. As machine learning becomes more intertwined in clinical settings, it is necessary that bias mitigation is accounted for, but performance maintenance remains a challenge. Fairness-aware interpretable modeling (FAIM) [1] is an in-processing fairness method that avoids extreme performance degradation while improving fairness and maintaining interpretability. In this study, the method is stress-tested by changing the original prediction task, hospital admission prediction after emergency department (ED) stay, to the distinct clinical task of predicting necessity of invasive medical ventilation (IMV) for patients in the intensive care unit (ICU) using electronic health record (EHR) data from the recently released MIMIC-IV database. A comparison with the baseline logistic regression model and other state-of-the-art fairness methods is presented and, although bias amongst intersectional demographic subgroups was not completely mitigated with FAIM, there was clear improvement compared to the baseline and also compared to other traditional fairness methods.