汉塔病毒监测：用于汉塔病毒基因组监测的联邦学习
HantaWatch: Federated Learning for Hantavirus Genomic Surveillance
浏览论文内容
中文总结 AI 辅助
针对汉塔病毒基因组监测受数据分布等限制的问题，提出HantaWatch联邦学习框架，集成多种技术，通过实验验证其能支持多任务并平衡多种性能，还可转化模型输出，为分散监测提供实用决策支持层。
中文摘要 AI 辅助
汉塔病毒基因组监测受到序列数据分布、非IID源异质性和有限的专家审查能力的限制。我们提出了HantaWatch，这是一个联邦学习框架，使实验室和监测站点能够在不共享原始数据的情况下协作训练基于序列的模型。HantaWatch集成了k-mer特征提取、源感知联邦客户端构建、自适应DU-FedProx优化、特定监测模型选择和仅预测分类。在二元和多类任务上的实验表明，HantaWatch支持高风险筛查、疫情相关预测、进化枝分类和临床综合征分类，同时平衡预测性能、假阴性风险和更新稳定性。该框架将模型输出转换为风险评分、置信估计、不确定性标志和排名专家审查优先级。因此，HantaWatch为分散的汉塔病毒监测提供了一个实用的联邦决策支持层，支持专家优先级排序，而不取代实验室或公共卫生解释。
英文摘要
Hantavirus genomic surveillance is limited by the distribution of sequence data, non-IID source heterogeneity, and constrained expert-review capacity. We propose HantaWatch, a federated learning framework that enables laboratories and surveillance sites to collaboratively train sequence-based models without sharing raw data. HantaWatch integrates k-mer feature extraction, source-aware federated client construction, adaptive DU-FedProx optimization, surveillance-specific model selection, and prediction-only triage. Experiments on binary and multi-class tasks show that HantaWatch supports high-risk screening, outbreak-associated prediction, clade classification, and clinical-syndrome categorization while balancing predictive performance, false-negative risk, and update stability. The framework converts model output into risk scores, confidence estimates, uncertainty flags, and ranked expert-review priorities. HantaWatch therefore provides a practical federated decision-support layer for decentralized Hantavirus surveillance, supporting expert prioritization without replacing laboratory or public-health interpretation.
发表机构
- School of IT, Deakin University（迪肯大学信息技术学院）
机构由 AI 辅助整理，请以论文原文为准。
