FairHealth: 一个用于低资源环境可信医疗AI的开源Python库
FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings
AI总结：
FairHealth通过整合公平性审计、隐私保护联邦学习、可解释性工具等模块，解决低资源医疗AI工具包的关键缺口，提升全球南医疗数据的可信度与公平性。
摘要：
我们介绍了FairHealth，一个开源Python库，提供统一的模块化框架，用于医疗应用中的可信机器学习，特别关注低资源和低收入国家（如孟加拉国）的环境。FairHealth解决了现有医疗AI工具包的四个关键缺口：（1）生物信号和临床表格数据缺乏整合公平性审计；（2）缺乏与标准ML工作流程兼容的隐私保护联邦学习工具；（3）缺乏针对低带宽临床决策支持的可解释性工具；（4）没有现有工具包涵盖全球南医疗数据集。FairHealth由五个同行评审的研究贡献构建，提供六个模块，包括使用同态加密的联邦学习（fairhealth.federated）、交叉公平性指标（fairhealth.fairness）、混合模糊-SHAP可解释性（fairhealth.explain）、多语言登革热分诊（fairhealth.lowresource）、公平灾害援助分配（fairhealth.equity）以及公共数据集加载器（fairhealth.datasets）。所有使用的数据集均公开可用，无需机构数据使用协议。FairHealth可通过pip install fairhealth（PyPI: pypi.org/project/fairhealth/）安装，并可在https://github.com/Farjana-Yesmin/fairhealth上获取。
英文摘要：
We present FairHealth, an open-source Python library that provides a unified, modular framework for trustworthy machine learning in healthcare applications, with particular focus on low-resource and low-income country (LMIC) settings such as Bangladesh. FairHealth addresses four critical gaps in existing healthcare AI toolkits: (1) the absence of integrated fairness auditing for biosignals and clinical tabular data; (2) the lack of privacy-preserving federated learning tools compatible with standard ML workflows; (3) missing explainability tools tailored for low-bandwidth clinical decision support; and (4) no existing toolkit covering Global South healthcare datasets. Built from five peer-reviewed research contributions, FairHealth provides six modules covering federated learning with homomorphic encryption (fairhealth.federated), intersectional fairness metrics (fairhealth.fairness), hybrid fuzzy-SHAP explainability (fairhealth.explain), multilingual dengue triage (fairhealth.lowresource), equitable disaster aid allocation (fairhealth.equity), and public dataset loaders (fairhealth.datasets). All datasets used are publicly available without institutional data use agreements. FairHealth is installable via pip install fairhealth(PyPI: pypi.org/project/fairhealth/) and available at https://github.com/Farjana-Yesmin/fairhealth.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
