欧洲远程医疗网络中支持重症监护室患者决策过程的联合学习方法

IF 3.3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Giovanni Paragliola, Patrizia Ribino, Zaib Ullah
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引用次数: 0

摘要

大流行病的一个结果是迫切需要数据合作,以增强临床和科学界应对快速发展的全球挑战的能力。ICU4Covid 项目将欧洲各地的研究机构、医疗中心和医院联合在一个远程医疗网络中,共享分布在网络中的能力、知识和专长。然而,医疗数据共享在伦理、监管和法律方面存在复杂性,对数据的访问和使用造成了一些限制。为了缓解这一问题,ICU4Covid 项目整合了一个联合学习架构,允许在跨机构医疗保健系统内进行分布式机器学习,而无需将数据传输或暴露在其原始位置之外。本文介绍了在欧洲远程医疗网络中支持重症监护室患者决策过程的联合学习方法。所提出的方法被应用于高危高血压患者的早期识别。实验结果表明,每个节点的知识是如何在联盟内传播的,从而提高了每个节点对高危高血压患者进行早期预测的能力。此外,性能评估显示准确率和精确率均超过 90%,证实了 FL 方法作为预测测试的良好性能。FL方法可为联合医疗机构分布式网络中的重症监护室患者决策过程提供重要支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network
A result of the pandemic is an urgent need for data collaborations that empower the clinical and scientific communities in responding to rapidly evolving global challenges. The ICU4Covid project joined research institutions, medical centers, and hospitals all around Europe in a telemedicine network for sharing capabilities, knowledge, and expertise distributed within the network. However, healthcare data sharing has ethical, regulatory, and legal complexities that pose several restrictions on their access and use. To mitigate this issue, the ICU4Covid project integrates a federated learning architecture, allowing distributed machine learning within a cross-institutional healthcare system without the data being transported or exposed outside their original location. This paper presents the federated learning approach to support the decision-making process for ICU patients in a European telemedicine network. The proposed approach was applied to the early identification of high-risk hypertensive patients. Experimental results show how the knowledge of every single node is spread within the federation, improving the ability of each node to make an early prediction of high-risk hypertensive patients. Moreover, a performance evaluation shows an accuracy and precision of over 90%, confirming a good performance of the FL approach as a prediction test. The FL approach can significantly support the decision-making process for ICU patients in distributed networks of federated healthcare organizations.
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来源期刊
Journal of Sensor and Actuator Networks
Journal of Sensor and Actuator Networks Physics and Astronomy-Instrumentation
CiteScore
7.90
自引率
2.90%
发文量
70
审稿时长
11 weeks
期刊介绍: Journal of Sensor and Actuator Networks (ISSN 2224-2708) is an international open access journal on the science and technology of sensor and actuator networks. It publishes regular research papers, reviews (including comprehensive reviews on complete sensor and actuator networks), and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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