Using Knowledge Graph for Analysis of Neglected Influencing Factors of Statin-Induced Myopathy

Yu, Yang; Huang, Zhisheng; Han, Yong; Hua, Xiaoli; Tang, Weijing

doi:10.1007/978-3-319-70772-3_29

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…automated algorithm: relation prediction Herb recommendation [137] TCM KG KRL: KGETM&HC-KGETM: entity prediction Prediction of DDIs [38] DrugBank GM: similarity measures ! logistic regression: relation prediction Drug-drug interaction prediction [8] DeepDDI, Decagon KRL: AAEs: relation prediction & classification Analysis of neglected influencing factors of statin-induced myopathy [110] LOD, LLD SP: SPARQL query: other analysis Drug discovery [80] SemKG GM: path exploration ! logistic regression: ranking Drug efficacy screening [138] Guney, EMC GM: path exploration !…”

Section: Clinical Educationmentioning

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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Section: Clinical Educationmentioning

confidence: 99%

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

Duan

Pan

et al. 2023

Big Data Min. Anal.

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“…The authors stated that the knowledge graph is still in its initial stage, and they wished to explore the construction method of the Chinese knowledge graph for cardiovascular disease. In another International Conference on Brain Informatics [18], authors demonstrated the use of a knowledge graph for the analysis of neglected influencing factors of statin-induced myopathy in a coronary heart disease case by using this new technology of AI.…”

Section: Application Of Medical Knowledge Graphs In Cardiology and Ca...mentioning

confidence: 99%

Application of Medical Knowledge Graphs in Cardiology and Cardiovascular Medicine: A Brief Literature Review

Wang

et al. 2022

Adv Ther

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A knowledge graph is defined as a collection of interlinked descriptions of concepts, relationships, entities and events. Medical knowledge graphs have been the most recent advances in technology, therapy and medicine. Nowadays, a number of specific uses and applications rely on knowledge graphs. The application of the knowledge graph, another form of artificial intelligence (AI) in cardiology and cardiovascular medicine, is a new concept, and only a few studies have been carried out on this particular aspect. In this brief literature review, the use and importance of disease-specific knowledge graphs in exploring various aspects of Kawasaki disease were described. A vision of individualized knowledge graphs (iKGs) in cardiovascular medicine was also discussed. Such iKGs would be based on a modern informatics platform of exchange and inquiry that could comprehensively integrate biologic knowledge with medical histories and health outcomes of individual patients. This could transform how clinicians and scientists discover, communicate and apply new knowledge. In addition, we also described how a study based on the comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease used a knowledge graph to show the dietary factors associated with cardiovascular diseases in Nurses’ Health Study data. To conclude, in this fast-developing world, medical knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. They could be a major and effective tool in cardiology and cardiovascular medicine and play an important role in reaching effective clinical decisions during treatment and management of patients in the cardiology department.

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“…Knowledge graphs are a powerful tool to bring together both structured and unstructured disparate data silos. A knowledge graph is a large-scale semantic network consisting of entities and concepts as well as the semantic relationships among them [14] thereby supporting making better decisions by searching for potential relationships faster. Knowledge graphs have been proven to be useful tools for integrating multiple medical knowledge sources and supporting such tasks as medical decision-making [15], literature retrieval [16], determining medical quality indicators [17], comorbidity analysis [18].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Microbiota-Gut-Brain Axis for Mental Disorders with Knowledge Graphs

Liu¹,

Pan²,

Wang³

et al. 2021

JoAIMS

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Using Knowledge Graph for Analysis of Neglected Influencing Factors of Statin-Induced Myopathy

Cited by 5 publications

References 18 publications

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

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

Application of Medical Knowledge Graphs in Cardiology and Cardiovascular Medicine: A Brief Literature Review

Exploring the Microbiota-Gut-Brain Axis for Mental Disorders with Knowledge Graphs

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