Blockchain-Enabled Federated Learning for Longitudinal Emergency Care

【Author】 Alshudukhi, Khulud Salem S.; Ashfaq, Farzeen; Jhanjhi, N. Z.; Humayun, Mamoona

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】Emergency situations, such as accidents, explosions, or earthquakes, pose significant challenges for healthcare providers as patients arrive at hospitals with little to no prior information about their medical history or treatment. Doctors frequently lack crucial information regarding past hospitalisations, surgeries, any allergies, previous treatments, and medical issues particularly when patients are alone and unable to communicate. Addressing these challenges requires a medical and technical solution that can rapidly access and incorporate a patient's past medical history while ensuring data privacy and security. By integrating these technologies, our framework not only ensures swift access to critical information but also maintains the highest standards of data privacy and security. To address this issue, we propose a framework that combines federated learning and blockchain technology to facilitate real-time emergency response while incorporating patient historical data. The proposed framework empowers doctors to make informed decisions and deliver personalized care based on a patient's comprehensive medical history, leading to improved treatment outcomes.

【Keywords】Medical services; Blockchains; History; Medical diagnostic imaging; Federated learning; Data privacy; Information exchange; Medical information systems; blockchain; smart healthcare; data security; health information sharing; longitudinal health record

【发表时间】2024

【收录时间】2024-10-13

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-医疗领域