Medical knowledge extraction methods based on edge computing deploy deep
learning models on edge devices to achieve localized entity and relation
extraction. This approach avoids transferring substantial sensitive data to
cloud data centers, effectively safeguarding the privacy of healthcare
services. However, existing relation extraction methods mainly employ a
sequential pipeline approach, which classifies relations between determined
entities after entity recognition. This mode faces challenges such as error
propagation between tasks, insufficient consideration of dependencies between
the two subtasks, and the neglect of interrelations between different relations
within a sentence. To address these challenges, a joint extraction model with
parameter sharing in edge computing is proposed, named CoEx-Bert. This model
leverages shared parameterization between two models to jointly extract
entities and relations. Specifically, CoEx-Bert employs two models, each
separately sharing hidden layer parameters, and combines these two loss
functions for joint backpropagation to optimize the model parameters.
Additionally, it effectively resolves the issue of entity overlapping when
extracting knowledge from unstructured Uyghur medical texts by considering
contextual relations. Finally, this model is deployed on edge devices for
real-time extraction and inference of Uyghur medical knowledge. Experimental
results demonstrate that CoEx-Bert outperforms existing state-of-the-art
methods, achieving accuracy, recall, and F1 scores of 90.65\%, 92.45\%, and
91.54\%, respectively, in the Uyghur traditional medical literature dataset.
These improvements represent a 6.45\% increase in accuracy, a 9.45\% increase
in recall, and a 7.95\% increase in F1 score compared to the baseline.

利用边缘计算部署基于深度学习模型的医疗知识提取方法，通过在边缘设备上实现本地化实体和关系提取，有效保护了卫生保健服务的隐私。提出了一种名为 CoEx-Bert 的联合提取模型，利用边缘计算中两个模型之间的参数共享来共同提取实体和关系。通过考虑上下文关系，有效解决了从非结构化维吾尔医学文本中提取知识时的实体重叠问题。实验结果表明，CoEx-Bert 在维吾尔传统医学文献数据集中的准确度、召回率和 F1 得分分别达到 90.65％、92.45％和 91.54％，相比基准模型分别提高了 6.45％、9.45％和 7.95％。