Personalized Federated Graph Learning on Non-IID Electronic Health Records

Understanding the latent disease patterns embedded in electronic health records (EHRs) is crucial for making precise and proactive healthcare decisions. Federated graph learning-based methods are commonly employed to extract complex disease patterns from the distributed EHRs without sharing the clie...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2024-09, Vol.35 (9), p.11843-11856
Hauptverfasser:	Tang, Tao, Han, Zhuoyang, Cai, Zhen, Yu, Shuo, Zhou, Xiaokang, Oseni, Taiwo, Das, Sajal K.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models Algorithms Data models Disease prediction Diseases electronic health record (EHR) Electronic Health Records Federated learning graph neural network (GNN) Humans Machine Learning Neural Networks, Computer non-independent and identically distributed (Non-IID) data personalized federated learning Predictive models Task analysis Training
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