利用边缘计算联合提取维吾尔医药知识

IF 6.6 1区 计算机科学 Q1 Multidisciplinary
Fan Lu;Quan Qi;Huaibin Qin
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引用次数: 0

摘要

本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint Extraction of Uygur Medicine Knowledge with Edge Computing
Edge computing, a novel paradigm for performing computations at the network edge, holds significant relevance in the healthcare domain for extracting medical knowledge from traditional Uygur medical texts. 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 Uygur medical texts by considering contextual relations. Finally, this model is deployed on edge devices for real-time extraction and inference of Uygur medical knowledge. Experimental results demonstrate that CoEx-Bert outperforms existing state-of-the-art methods, achieving accuracy, recall, and F1-score of 90.65%, 92.45%, and 91.54%, respectively, in the Uygur 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.
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来源期刊
Tsinghua Science and Technology
Tsinghua Science and Technology COMPUTER SCIENCE, INFORMATION SYSTEMSCOMPU-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
10.20
自引率
10.60%
发文量
2340
期刊介绍: Tsinghua Science and Technology (Tsinghua Sci Technol) started publication in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, electronic engineering, and other IT fields. Contributions all over the world are welcome.
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