医疗保健中的公平性：分析糖尿病患者再入院机器学习预测中的差异
Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions
AI总结：
本研究比较多种机器学习模型预测糖尿病患者再入院，发现梯度提升机（GBM）在准确率与公平性上表现最佳，能有效减少跨性别、种族和年龄的预测偏差。
摘要：
本研究探讨了机器学习（ML）模型如何在不同人口统计学特征（年龄、性别、种族）下公平且准确地预测糖尿病患者的医院再入院情况。我们比较了深度学习、广义线性模型、梯度提升机（GBM）和朴素贝叶斯等模型。GBM表现突出，F1分数为84.3%，准确率为82.2%，能够准确预测不同人口统计学群体的再入院情况。对所有模型进行了公平性分析。GBM最小化了预测中的差异，在性别和种族之间实现了平衡的结果。它在两种性别上均表现出较低的假发现率（FDR）（6-7%）和假阳性率（FPR）（5%）。此外，种族群体的FDR也保持在较低水平，例如非裔美国人（8%）和亚裔（7%）。同样，对于40岁以下和40岁以上的患者，各年龄组的FPR保持一致（4%），表明其精确性和减少偏差的能力。这些发现强调了谨慎选择ML模型以确保所有患者的准确性和公平性的重要性。通过展示各种模型在公平性指标下的有效性，本研究推动了个性化医疗的发展，并强调了医疗保健中对公平ML算法的需求。这最终可以减少差异，并改善所有背景的糖尿病患者的预后。
英文摘要：
This study investigates how machine learning (ML) models can predict hospital readmissions for diabetic patients fairly and accurately across different demographics (age, gender, race). We compared models like Deep Learning, Generalized Linear Models, Gradient Boosting Machines (GBM), and Naive Bayes. GBM stood out with an F1-score of 84.3% and accuracy of 82.2%, accurately predicting readmissions across demographics. A fairness analysis was conducted across all the models. GBM minimized disparities in predictions, achieving balanced results across genders and races. It showed low False Discovery Rates (FDR) (6-7%) and False Positive Rates (FPR) (5%) for both genders. Additionally, FDRs remained low for racial groups, such as African Americans (8%) and Asians (7%). Similarly, FPRs were consistent across age groups (4%) for both patients under 40 and those above 40, indicating its precision and ability to reduce bias. These findings emphasize the importance of choosing ML models carefully to ensure both accuracy and fairness for all patients. By showcasing effectiveness of various models with fairness metrics, this study promotes personalized medicine and the need for fair ML algorithms in healthcare. This can ultimately reduce disparities and improve outcomes for diabetic patients of all backgrounds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
