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.

使用MIMIC-IV数据集研究XGBoost二元分类模型在预测ICU住院时间方面的公平性和偏见，并强调该研究对ICU有限资源分配的重要性，通过在人口属性上检测数据集的类别不平衡，并采用数据预处理和特征提取，发现XGBoost模型整体表现良好，但是在种族和保险属性上出现不平等，提出了关于缓解偏见的关注公平机器学习技术的建议，并强调医疗保健专业人员和数据科学家之间的合作努力的必要性。

评估MIMIC-IV数据集和基准算法的公平性：应用于ICU住院时间预测