针对呼吸疾病诊断模型中的性别偏差，提出有效缓解方法。
Mitigating Sex Bias in Audio Data-driven COPD and COVID-19 Breathing Pattern Detection Models
- 用决策树模型分析音频数据中的性别偏差
- 通过约束优化实现81.43%和71.81%的偏差改善
- 适合关注医疗AI公平性的研究者与从业者
在医疗领域，研究人员正利用机器学习模型基于呼吸模式自动诊断呼吸道疾病。然而，当训练数据集患者性别分布不均时，模型常存在性别偏差，影响诊断公平性。本文聚焦慢性阻塞性肺病（COPD）和新冠肺炎（COVID-19）的呼吸模式检测模型，使用包含29例COPD和680例新冠阳性患者的两个开源音频数据集，训练决策树模型并分析性别偏差影响。通过阈值优化器及人口均等性和等机会两个约束条件进行偏差缓解，实现81.43%（人口均等性差异）和71.81%（等机会差异）的性能提升，结果具有统计显著性。
原文摘要 · Abstract (English)
In the healthcare industry, researchers have been developing machine learning models to automate diagnosing patients with respiratory illnesses based on their breathing patterns. However, these models do not consider the demographic biases, particularly sex bias, that often occur when models are trained with a skewed patient dataset. Hence, it is essential in such an important industry to reduce this bias so that models can make fair diagnoses. In this work, we examine the bias in models used to detect breathing patterns of two major respiratory diseases, i.e., chronic obstructive pulmonary disease (COPD) and COVID-19. Using decision tree models trained with audio recordings of breathing patterns obtained from two open-source datasets consisting of 29 COPD and 680 COVID-19-positive patients, we analyze the effect of sex bias on the models. With a threshold optimizer and two constraints (demographic parity and equalized odds) to mitigate the bias, we witness 81.43% (demographic parity difference) and 71.81% (equalized odds difference) improvements. These findings are statistically significant.
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