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Medical knowledge extraction methods based on edge computing deploy deeplearning models on edge devices to achieve localized entity and relationextraction. This approach avoids transferring substantial sensitive data tocloud data centers, effectively safeguarding the privacy of healthcareservices. However, existing relation extraction methods mainly employ asequential pipeline approach, which classifies relations between determinedentities after entity recognition. This mode faces challenges such as errorpropagation between tasks, insufficient consideration of dependencies betweenthe two subtasks, and the neglect of interrelations between different relationswithin a sentence. To address these challenges, a joint extraction model withparameter sharing in edge computing is proposed, named CoEx-Bert. This modelleverages shared parameterization between two models to jointly extractentities and relations. Specifically, CoEx-Bert employs two models, eachseparately sharing hidden layer parameters, and combines these two lossfunctions for joint backpropagation to optimize the model parameters.Additionally, it effectively resolves the issue of entity overlapping whenextracting knowledge from unstructured Uyghur medical texts by consideringcontextual relations. Finally, this model is deployed on edge devices forreal-time extraction and inference of Uyghur medical knowledge. Experimentalresults demonstrate that CoEx-Bert outperforms existing state-of-the-artmethods, achieving accuracy, recall, and F1 scores of 90.65\%, 92.45\%, and91.54\%, respectively, in the Uyghur traditional medical literature dataset.These improvements represent a 6.45\% increase in accuracy, a 9.45\% increasein recall, and a 7.95\% increase in F1 score compared to the baseline.

Joint Extraction of Uyghur Medicine Knowledge with Edge Computing