用联邦学习实现新生儿窒息的隐私保护早期检测
HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning
- 基于联邦学习构建分布式模型，数据不出机构即可联合训练
- 在多中心数据上验证，联邦支持向量机性能优于集中式模型和神经网络
- 专为非洲医疗条件设计，低成本移动应用，适合资源匮乏地区使用
新生儿窒息（BA）是在分娩过程中新生儿氧气供应不足的严重状况，是全球新生儿死亡的主要原因之一。尽管过去二十年新生儿死亡率有所下降，但撒哈拉以南非洲等发展中国家仍面临最高的五岁以下儿童死亡率。虽然非洲医疗环境中普遍采用循证方法检测BA，但常因医生误判或诊断延迟而错失及时干预时机。集中式机器学习方法虽在早期检测中表现良好，但需将敏感健康数据移出机构进行训练，难以保障隐私与安全，导致医疗机构不愿采纳。为此，我们提出一种基于联邦学习（FL）的软件架构，通过分布式学习机制从源头保障隐私与安全。开发了一款用户友好、成本低廉的移动端应用，集成联邦学习流程，用于早期检测新生儿窒息。实验结果表明，我们的联邦支持向量机模型在多中心数据集上的表现优于现有的集中式支持向量机及神经网络方法。
原文摘要 · Abstract (English)
Birth Apshyxia (BA) is a severe condition characterized by insufficient supply of oxygen to a newborn during the delivery. BA is one of the primary causes of neonatal death in the world. Although there has been a decline in neonatal deaths over the past two decades, the developing world, particularly sub-Saharan Africa, continues to experience the highest under-five (<5) mortality rates. While evidence-based methods are commonly used to detect BA in African healthcare settings, they can be subject to physician errors or delays in diagnosis, preventing timely interventions. Centralized Machine Learning (ML) methods demonstrated good performance in early detection of BA but require sensitive health data to leave their premises before training, which does not guarantee privacy and security. Healthcare institutions are therefore reluctant to adopt such solutions in Africa. To address this challenge, we suggest a federated learning (FL)-based software architecture, a distributed learning method that prioritizes privacy and security by design. We have developed a user-friendly and cost-effective mobile application embedding the FL pipeline for early detection of BA. Our Federated SVM model outperformed centralized SVM pipelines and Neural Networks (NN)-based methods in the existing literature
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