轻量级联合学习用于性传播感染/艾滋病毒预测
Thi Phuoc Van Nguyen1, Wencheng Yang2, Zhaohui Tang2
1School of Mathematics, Physics and Computing, Centre for Health Research, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350, QLD, Australia. phuocvan.haui@gmail.com.
Scientific reports
|March 20, 2024
概括
这项研究引入了联合学习 (FL) 来预测性传播感染/人类免疫缺陷病毒 (STIs/HIV) 风险,增强隐私和沟通. 随机森林FL模型实现了高精度,优于现有方法.
科学领域:
- 计算机科学 计算机科学
- 公共卫生 公共卫生
- 生物医学信息学 生物医学信息学
背景情况:
- 预测性传播感染/人类免疫缺陷病毒 (STI/HIV) 风险对于公共卫生干预至关重要.
- 现有的方法经常面临数据隐私和通信效率方面的挑战.
- 联合学习 (FL) 提供了一种去中心化的机器学习方法,保护数据隐私.
研究的目的:
- 开发和评估一个保护隐私的模型,用联邦学习来预测性传播感染/艾滋病毒风险.
- 改善医疗应用分布式机器学习环境中的通信吞吐量.
- 评估随机森林联合学习方法在性传播感染/艾滋病毒风险估计方面的表现.
主要方法:
- 利用联合学习 (FL) 来训练在多个诊所的预测模型,而无需共享原始患者数据.
- 在FL框架内实施了一种随机森林算法,用于STD/HIV风险评估.
- 开发了一种灵活的聚合过程,以适应系统中的不同通信能力.
主要成果:
- 拟议的随机森林FL模型在估计性传播感染/艾滋病毒风险方面显示出显著的潜力.
- 与最近的研究相比,获得了优异的性能,AUC为0.97和高精度.
- 灵活的聚合过程提高了系统通信吞吐量.
结论:
- 联合学习为保护隐私的性传播感染/艾滋病毒风险预测提供了强大的解决方案.
- 随机森林FL方法为改善公共卫生结果提供了一个有希望和有效的方法.
- 未来的研究应该探索高风险人群,以进一步验证框架的影响.
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