Li Na, Lewin Antoine, Ning Shuoyan, Waito Marianne, Zeller Michelle P, Tinmouth Alan, Shih Andrew W
Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada.
Transfusion. 2025 Jan;65(1):22-28. doi: 10.1111/trf.18077. Epub 2024 Nov 29.
Health data comprise data from different aspects of healthcare including administrative, digital health, and research-oriented data. Together, health data contribute to and inform healthcare operations, patient care, and research. Integrating artificial intelligence (AI) into healthcare requires understanding these data infrastructures and addressing challenges such as data availability, privacy, and governance. Federated learning (FL), a decentralized AI training approach, addresses these challenges by allowing models to learn from diverse datasets without data leaving its source, thus ensuring privacy and security are maintained. This report introduces FL and discusses its potential in transfusion medicine and blood supply chain management.
FL can offer significant benefits in transfusion medicine by enhancing predictive analytics, personalized medicine, and operational efficiency. Predictive models trained on diverse datasets by FL can improve accuracy in forecasting blood transfusion demands. Personalized treatment plans can be refined by aggregating patient data from multiple institutions using FL, reducing adverse reactions and improving outcomes. Operational efficiency can also be achieved through precise demand forecasting and optimized logistics. Despite its advantages, FL faces challenges such as data standardization, governance, and bias. Harmonizing diverse data sources and ensuring fair, unbiased models require advanced analytical solutions. Robust IT infrastructure and specialized expertise are needed for successful FL implementation.
FL represents a transformative approach to AI development in healthcare, particularly in transfusion medicine. By leveraging diverse datasets while maintaining data privacy, FL has the potential to enhance predictions, support personalized treatments, and optimize resource management, ultimately improving patient care and healthcare efficiency.
健康数据包括来自医疗保健不同方面的数据，如管理数据、数字健康数据和研究导向型数据。健康数据共同为医疗保健运营、患者护理和研究提供支持并提供信息。将人工智能（AI）整合到医疗保健领域需要了解这些数据基础设施，并应对数据可用性、隐私和治理等挑战。联邦学习（FL）是一种去中心化的AI训练方法，通过允许模型在不将数据移出其来源的情况下从不同数据集中学习来应对这些挑战，从而确保隐私和安全得到维护。本报告介绍了联邦学习，并讨论了其在输血医学和血液供应链管理中的潜力。
联邦学习可以通过增强预测分析、个性化医疗和运营效率，在输血医学中带来显著益处。通过联邦学习在不同数据集上训练的预测模型可以提高预测输血需求的准确性。通过使用联邦学习汇总来自多个机构的患者数据，可以完善个性化治疗方案，减少不良反应并改善治疗效果。还可以通过精确的需求预测和优化的物流实现运营效率。尽管有其优势，但联邦学习面临数据标准化、治理和偏差等挑战。协调不同数据源并确保公平、无偏差的模型需要先进的分析解决方案。成功实施联邦学习需要强大的IT基础设施和专业知识。
联邦学习代表了医疗保健领域人工智能发展的一种变革性方法，特别是在输血医学中。通过利用不同数据集同时保持数据隐私，联邦学习有潜力增强预测、支持个性化治疗并优化资源管理，最终改善患者护理和医疗保健效率。
