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The Mouse Stroke Unit Protocol with Standardized Neurological Scoring for Translational Mouse Stroke Studies
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PSA-FL-CDM:一种新的基于学习的联合共识模型,用于中风后评估.

Najmeh Razfar1, Rasha Kashef1, Farah Mohammadi1

  • 1Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括
此摘要是机器生成的。

联合学习增强了使用物联网数据进行中风后评估,同时保护了患者的隐私. 与集中式模型相比,这种AI方法显著减少了计算时间,并保持了性能.

关键词:
在PSA_MNMF模型中.摄像机基础数据集达成共识的聚类聚类.联合学习的联合学习评估中风评估中风的评估.可以穿戴的数据集.

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科学领域:

  • 医疗保健中的人工智能
  • 物联网 (IoT) 用于康复
  • 医疗AI中的数据隐私

背景情况:

  • 物联网 (IoT) 设备产生了用于智能康复的庞大数据集.
  • 患者隐私在医疗保健中至关重要,尤其是敏感数据.
  • 目前用于中风评估的AI模型可能无法充分保护隐私.

研究的目的:

  • 提出一个可扩展的AI模型,使用联合学习进行中风后评估.
  • 在智能康复数据分析期间保护患者隐私.
  • 将联合学习与集中式模型的性能进行比较.

主要方法:

  • 采用联合学习 (PSA-FL-CDM) 进行可扩展的AI模型开发.
  • 与基于传感器和摄像头的数据集的集中模型 (PSA-MNMF) 的比较.
  • 在每个节点上实施共识模型和八种集群方法.
  • 利用FedAvg算法用于全球模型创建.

主要成果:

  • 联合的PSA-FL-CDM模型显著减少了计算时间.
  • 在保持患者隐私的同时,通过联合模型实现了可比性能.
  • 联合学习使协作模型培训能够在不共享原始数据的情况下进行.
  • 在可穿戴设备和基于摄像头的数据集上进行的实验.

结论:

  • 联合学习为人工智能驱动的中风评估提供了一个可扩展和保护隐私的解决方案.
  • 拟议的PSA-FL-CDM模型证明了效率和可比的准确性.
  • 这种方法促进了协作医疗分析,同时维护了数据保密性.