KeMRE: Knowledge-enhanced medical relation extraction for Chinese medicine instructions

Qi, Tao; Qiu, Shan; Shen, Xiaoqian; Chen, Haopu; Yang, Shuai; Wen, Hao; Zhang, Ya; Wu, Yuanqing; Huang, Yudong

doi:10.1016/j.jbi.2021.103834

Cited by 18 publications

(6 citation statements)

References 14 publications

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“…However, their application in the field of TCM is still relatively limited. Most research has focused on entity and relationship extraction methods, as well as the development of model algorithms ( Qi et al, 2021 ; Jin et al, 2023 ). These research methods provide excellent technical support for discovering and reasoning TCM-related diagnosis and treatment knowledge.…”

Section: Discussionmentioning

confidence: 99%

“…They offer essential technical means to integrate, share, and deeply explore relevant knowledge on disease diagnosis and treatment. However, most research in this area tends to focus on technical methods, such as optimizing and embedding algorithms like Named Entity Recognition and Disambiguation ( Ma et al, 2021 ; Li et al, 2022 ; Kurbatova and Swiers, 2021 ; Qi et al, 2021 ). There is still a limited number of studies addressing practical clinical problems using knowledge graph technology, and specific challenges remain in the current research on medical knowledge graphs.…”

Section: Introductionmentioning

confidence: 99%

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The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data

Zhao

Wang

Li³

et al. 2023

Front. Pharmacol.

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Background: The complexity and rapid progression of lesions in diabetic kidney disease pose significant challenges for clinical diagnosis and treatment. The advantages of Traditional Chinese Medicine (TCM) in diagnosing and treating this condition have gradually become evident. However, due to the disease’s complexity and the individualized approach to diagnosis and treatment in Traditional Chinese Medicine, Traditional Chinese Medicine guidelines have limitations in guiding the treatment of diabetic kidney disease. Most medical knowledge is currently stored in the process of recording medical records, which hinders the understanding of diseases and the acquisition of diagnostic and treatment knowledge among young doctors. Consequently, there is a lack of sufficient clinical knowledge to support the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine.Objective: To build a comprehensive knowledge graph for the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine, utilizing clinical guidelines, consensus, and real-world clinical data. On this basis, the knowledge of Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was systematically combed and mined.Methods: Normative guideline data and actual medical records were used to construct a knowledge graph of Traditional Chinese Medicine diagnosis and treatment for diabetic kidney disease and the results obtained by data mining techniques enrich the relational attributes. Neo4j graph database was used for knowledge storage, visual knowledge display, and semantic query. Utilizing multi-dimensional relations with hierarchical weights as the core, a reverse retrieval verification process is conducted to address the critical problems of diagnosis and treatment put forward by experts.Results: 903 nodes and 1670 relationships were constructed under nine concepts and 20 relationships. Preliminarily a knowledge graph for Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was constructed. Based on the multi-dimensional relationships, the diagnosis and treatment questions proposed by experts were validated through multi-hop queries of the graphs. The results were confirmed by experts and showed good outcomes.Conclusion: This study systematically combed the Traditional Chinese Medicine diagnosis and treatment knowledge of diabetic kidney disease by constructing the knowledge graph. Furthermore, it effectively solved the problem of “knowledge island”. Through visual display and semantic retrieval, the discovery and sharing of diagnosis and treatment knowledge of diabetic kidney disease were realized.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data

Zhao

Wang

Li³

et al. 2023

Front. Pharmacol.

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“…MLP is the steps in reducing the error function defined on the learning set (𝑥𝑥 𝑖𝑖 , 𝑑𝑑 𝑖𝑖 ). Knowledgeenhanced Medical Relations Extraction (KemRE) for medical guidance uses the MLP method [32]. The formula follows.…”

Section: Mlp Methodsmentioning

confidence: 99%

A comparison of machine learning methods for knowledge extraction model in A LoRa-Based waste bin monitoring system

Abidin,

Othman,

Hassan

et al. 2024

Int. J. Adv. Intell. Informatics

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Knowledge Extraction Model (KEM) is a system that extracts knowledge through an IoT-based smart waste bin emptying scheduling classification. Classification is a difficult problem and requires an efficient classification method. This research contributes in the form of the KEM system in the classification of scheduling for emptying waste bins with the best performance of the Machine Learning method. The research aims to compare the performance of Machine Learning methods in the form of Decision Tree, Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, and Multi-Layer Perceptron, which will be recommended in the KEM system. Performance testing was performed on accuracy, recall, precision, F-Measure, and ROCS curves using the cross-validation method with ten observations. The experimental results show that the Decision Tree performs best for accuracy, recall, precision, and ROCS curve. In contrast, the K-NN method obtains the highest F-measure performance. KEM can be implemented to extract knowledge from data sets created in various other IoT-based systems.

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“…attentation ! FGM: classification Semantic enhancement [11,87,141,142] -been synthesized. However, their description framework, ontology model, medical terminologies, semantic identities, and storage technologies are all distinct, which results in extremely difficult further reuse, linking, and sharing.…”

Section: Intelligent Nursingmentioning

confidence: 99%

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Duan

Pan

et al. 2023

Big Data Min. Anal.

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Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

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KeMRE: Knowledge-enhanced medical relation extraction for Chinese medicine instructions

Cited by 18 publications

References 14 publications

The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data

The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data

A comparison of machine learning methods for knowledge extraction model in A LoRa-Based waste bin monitoring system

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

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