通过优化多域对抗网络,提高疾病预测的公平性
Bin Li1, Xinghua Shi1, Hongchang Gao1
1Computer and Information Sciences, Temple University, Philadelphia, Pennsylvania, 19122, USA.
bioRxiv : the preprint server for biology
|August 23, 2023
概括
这项研究引入了多域对抗神经网络 (MDANN),以减少医学AI的偏见. 新的框架确保了对所有患者群体的更公平,更准确的疾病预测.
科学领域:
- 生物医学信息学是生物医学信息学.
- 机器学习在医疗保健中的应用
- 人工智能用于疾病预测.
背景情况:
- 生物医学预测模型需要公平可靠的结果.
- 医学预测中的算法偏差加剧了健康差异.
- 解决偏见对于公平有效的医疗保健应用至关重要.
结论:
- MDANN方法提供了可靠和公平的疾病预测.
- 在使用脑成像数据预测阿尔茨海默氏症和自闭症进展方面表现出更好的准确性和公平性.
- 突出了对抗性学习的潜力和对公正的生物医学AI的深度代表性.
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