Evaluating the Fairness of the MIMIC-IV Dataset and a Baseline
  Algorithm: Application to the ICU Length of Stay Prediction
Alexandra Kakadiaris
This paper uses the MIMIC-IV dataset to examine the fairness and bias in an
XGBoost binary classification model predicting the Intensive Care Unit (ICU)
length of stay (LOS). Highlighting the critical role of the ICU in managing
critically ill patients, the study addresses the growing strain on ICU
capacity. It emphasizes the significance of LOS prediction for resource
allocation. The research reveals class imbalances in the dataset across
demographic attributes and employs data preprocessing and feature extraction.
While the XGBoost model performs well overall, disparities across race and
insurance attributes reflect the need for tailored assessments and continuous
monitoring. The paper concludes with recommendations for fairness-aware machine
learning techniques for mitigating biases and the need for collaborative
efforts among healthcare professionals and data scientists.