Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine

被引：0
|
作者：
Li, Na [1 ,2 ,3 ]
Lewin, Antoine [4 ]
Ning, Shuoyan [3 ,5 ,6 ,7 ]
Waito, Marianne [8 ]
Zeller, Michelle P. [3 ,5 ,6 ,7 ]
Tinmouth, Alan [9 ,10 ]
Shih, Andrew W. [5 ,11 ]
机构：
[1] Univ Calgary, Cumming Sch Med, Dept Community Hlth Sci, Calgary, AB, Canada
[2] McMaster Univ, Dept Comp & Software, Hamilton, ON, Canada
[3] McMaster Univ, Michael G DeGroote Ctr Transfus Res, Hamilton, ON, Canada
[4] Hema Quebec, Med Affairs & Innovat, Montreal, PQ, Canada
[5] McMaster Univ, Dept Pathol & Mol Med, Hamilton, ON, Canada
[6] McMaster Univ, Michael G DeGroote Sch Med, Dept Med, Hamilton, ON, Canada
[7] Canadian Blood Serv, Ancaster, ON, Canada
[8] Canadian Blood Serv, Transplantat Serv, Ottawa, ON, Canada
[9] Ottawa Hosp, Dept Med, Ottawa, ON, Canada
[10] Ottawa Hosp, Res Inst, Ottawa, ON, Canada
[11] Canadian Blood Serv, Ctr Innovat, Ottawa, ON, Canada
来源：
TRANSFUSION | 2024年
基金：
加拿大自然科学与工程研究理事会;
关键词：
blood management; health research methodology; statistics; study design;
D O I：
10.1111/trf.18077
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
BackgroundHealth 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.Methods and DiscussionFL 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.ConclusionFL 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.
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页数：7
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