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Found 3 papers, 2 papers with code
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
In addition, the ML models trained with DeCaPH framework in general outperform those trained solely with the private datasets from individual parties, showing that DeCaPH enhances the model generalizability.
FENDA-FL: Personalized Federated Learning on Heterogeneous Clinical Datasets
Federated learning (FL) is increasingly being recognized as a key approach to overcoming the data silos that so frequently obstruct the training and deployment of machine-learning models in clinical settings.
Assessing the impact of emergency department short stay units using length-of-stay prediction and discrete event simulation
Accurately predicting hospital length-of-stay at the time a patient is admitted to hospital may help guide clinical decision making and resource allocation.
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