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

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          Abstract

          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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          Most cited references20

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          A Review of Relational Machine Learning for Knowledge Graphs

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            Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.

            Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation.
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              Identifying important risk factors for survival in patient with systolic heart failure using random survival forests.

              Heart failure survival models typically are constructed using Cox proportional hazards regression. Regression modeling suffers from a number of limitations, including bias introduced by commonly used variable selection methods. We illustrate the value of an intuitive, robust approach to variable selection, random survival forests (RSF), in a large clinical cohort. RSF are a potentially powerful extensions of classification and regression trees, with lower variance and bias. We studied 2231 adult patients with systolic heart failure who underwent cardiopulmonary stress testing. During a mean follow-up of 5 years, 742 patients died. Thirty-nine demographic, cardiac and noncardiac comorbidity, and stress testing variables were analyzed as potential predictors of all-cause mortality. An RSF of 2000 trees was constructed, with each tree constructed on a bootstrap sample from the original cohort. The most predictive variables were defined as those near the tree trunks (averaged over the forest). The RSF identified peak oxygen consumption, serum urea nitrogen, and treadmill exercise time as the 3 most important predictors of survival. The RSF predicted survival similarly to a conventional Cox proportional hazards model (out-of-bag C-index of 0.705 for RSF versus 0.698 for Cox proportional hazards model). An RSF model in a cohort of patients with heart failure performed as well as a traditional Cox proportional hazard model and may serve as a more intuitive approach for clinicians to identify important risk factors for all-cause mortality.
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                Author and article information

                Contributors
                iriswh2014@163.com
                59474566@qq.com
                China.luming_cn@tju.edu.cn
                rongfa.chen@ia.ac.cn
                zhiren.yang@ia.ac.cn
                1737883971@qq.com
                liuhaomd2013@163.com
                Journal
                Adv Ther
                Adv Ther
                Advances in Therapy
                Springer Healthcare (Cheshire )
                0741-238X
                1865-8652
                30 July 2022
                30 July 2022
                2022
                : 39
                : 9
                : 4052-4060
                Affiliations
                [1 ]GRID grid.410652.4, ISNI 0000 0004 6003 7358, Department of Cardiology, , The People’s Hospital of Guangxi Zhuang Autonomous Region, ; Nanning, 530021 Guangxi People’s Republic of China
                [2 ]GRID grid.258164.c, ISNI 0000 0004 1790 3548, Jinan University, ; Guangzhou, 510632 Guangdong People’s Republic of China
                [3 ]GRID grid.33763.32, ISNI 0000 0004 1761 2484, College of Management and Economics, , Tianjin University, ; Tianjin, 300072 People’s Republic of China
                [4 ]GRID grid.9227.e, ISNI 0000000119573309, The State Key Laboratory Management and Control for Complex Systems, Institute of Automation, , Chinese Academy of Sciences, ; Beijing, 100190 People’s Republic of China
                [5 ]GRID grid.411607.5, Department of Internal Medicine, , Beijing Chaoyang Hospital, ; Chaoyang, Beijing, 100020 People’s Republic of China
                Article
                2254
                10.1007/s12325-022-02254-7
                9402764
                35908002
                5314c717-abc8-413c-a24f-52607fad2be5
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 3 June 2022
                : 29 June 2022
                Categories
                Review
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                © Springer Healthcare Ltd., part of Springer Nature 2022

                medical knowledge graphs,advances in therapy,cardiology,cardiovascular diseases,artificial intelligence

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