研究医疗疼痛检测算法在性别上的公平性，发现所有模型均有偏差。
Gender Fairness of Machine Learning Algorithms for Pain Detection
- 用面部表情数据对比传统与深度学习模型的性别公平性
- ViT准确率最高，但各模型均存在性别偏差
- 适合关注医疗AI公平性的研究人员和开发者
通过机器学习（ML）和深度学习（DL）算法实现自动化疼痛检测，在医疗中具有重要潜力，尤其适用于无法自述疼痛的患者。然而，这些算法在不同人口群体（如性别）中的准确性和公平性仍缺乏充分研究。本文基于UNBC-McMaster肩部疼痛表情数据库，评估了多种模型仅凭面部表情视觉模态检测疼痛的性能与性别公平性。比较了线性支持向量机（L SVM）与径向基函数支持向量机（RBF SVM）等传统方法，以及卷积神经网络（CNN）和视觉变换器（ViT）等深度学习方法。尽管ViT在准确率和多项公平性指标上表现最优，但所有模型均表现出显著的性别偏差。结果凸显了准确性与公平性之间的持续权衡，强调需采用公平感知技术以减轻自动化医疗系统中的偏见。
原文摘要 · Abstract (English)
Automated pain detection through machine learning (ML) and deep learning (DL) algorithms holds significant potential in healthcare, particularly for patients unable to self-report pain levels. However, the accuracy and fairness of these algorithms across different demographic groups (e.g., gender) remain under-researched. This paper investigates the gender fairness of ML and DL models trained on the UNBC-McMaster Shoulder Pain Expression Archive Database, evaluating the performance of various models in detecting pain based solely on the visual modality of participants' facial expressions. We compare traditional ML algorithms, Linear Support Vector Machine (L SVM) and Radial Basis Function SVM (RBF SVM), with DL methods, Convolutional Neural Network (CNN) and Vision Transformer (ViT), using a range of performance and fairness metrics. While ViT achieved the highest accuracy and a selection of fairness metrics, all models exhibited gender-based biases. These findings highlight the persistent trade-off between accuracy and fairness, emphasising the need for fairness-aware techniques to mitigate biases in automated healthcare systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。