This study presents a machine learning (ML) pipeline for clinical data classification in the context of a 30-day readmission problem, along with a fairness audit on subgroups based on sensitive attributes. A range of ML models are used for classification and the fairness audit is conducted on the model predictions. The fairness audit uncovers disparities in equal opportunity, predictive parity, false positive rate parity, and false negative rate parity criteria on the MIMIC III dataset based on attributes such as gender, ethnicity, language, and insurance group. The results identify disparities in the model's performance across different groups and highlights the need for better fairness and bias mitigation strategies. The study suggests the need for collaborative efforts among researchers, policymakers, and practitioners to address bias and fairness in artificial intelligence (AI) systems.

翻译：本研究针对30天内再入院问题，构建了一个用于临床数据分类的机器学习（ML）流程，并对基于敏感属性的子群体进行了公平性审计。研究采用多种机器学习模型进行分类，并对模型预测结果进行公平性评估。基于MIMIC III数据集，公平性审计揭示了在性别、种族、语言及保险类型等属性维度上，模型在机会均等性、预测均等性、假阳性率均等性及假阴性率均等性准则方面存在差异。研究结果发现模型在不同群体间的性能表现存在差异，凸显了改进公平性与偏差缓解策略的必要性。该研究建议，研究者、政策制定者及从业者需协同合作，共同应对人工智能系统中的偏差与公平性问题。