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      Knowledge Graphs of Kawasaki Disease

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          Abstract

          Kawasaki Disease is a vasculitis syndrome that is extremely harmful to children. Kawasaki Disease can cause severe symptoms of ischemic heart disease or develop into ischemic heart disease, leading to death in children. Researchers and clinicians need to analyze various knowledge and data resources to explore aspects of Kawasaki Disease. Knowledge Graphs have become an important AI approach to integrating various types of complex knowledge and data resources. In this paper, we present an approach for the construction of Knowledge Graphs of Kawasaki Disease. It integrates a wide range of knowledge resources related to Kawasaki Disease, including clinical guidelines, clinical trials, drug knowledge bases, medical literature, and others. It provides a basic integration foundation of knowledge and data concerning Kawasaki Disease for clinical study. In this paper, we will show that this disease-specific Knowledge Graphs are useful for exploring various aspects of Kawasaki Disease.

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          Most cited references8

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          An overview of MetaMap: historical perspective and recent advances.

          MetaMap is a widely available program providing access to the concepts in the unified medical language system (UMLS) Metathesaurus from biomedical text. This study reports on MetaMap's evolution over more than a decade, concentrating on those features arising out of the research needs of the biomedical informatics community both within and outside of the National Library of Medicine. Such features include the detection of author-defined acronyms/abbreviations, the ability to browse the Metathesaurus for concepts even tenuously related to input text, the detection of negation in situations in which the polarity of predications is important, word sense disambiguation (WSD), and various technical and algorithmic features. Near-term plans for MetaMap development include the incorporation of chemical name recognition and enhanced WSD.
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            From SHIQ and RDF to OWL: the making of a Web Ontology Language

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              Anatomical entity mention recognition at literature scale

              Motivation: Anatomical entities ranging from subcellular structures to organ systems are central to biomedical science, and mentions of these entities are essential to understanding the scientific literature. Despite extensive efforts to automatically analyze various aspects of biomedical text, there have been only few studies focusing on anatomical entities, and no dedicated methods for learning to automatically recognize anatomical entity mentions in free-form text have been introduced. Results: We present AnatomyTagger, a machine learning-based system for anatomical entity mention recognition. The system incorporates a broad array of approaches proposed to benefit tagging, including the use of Unified Medical Language System (UMLS)- and Open Biomedical Ontologies (OBO)-based lexical resources, word representations induced from unlabeled text, statistical truecasing and non-local features. We train and evaluate the system on a newly introduced corpus that substantially extends on previously available resources, and apply the resulting tagger to automatically annotate the entire open access scientific domain literature. The resulting analyses have been applied to extend services provided by the Europe PubMed Central literature database. Availability and implementation: All tools and resources introduced in this work are available from http://nactem.ac.uk/anatomytagger. Contact: sophia.ananiadou@manchester.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                z.huang@vu.nl
                68484088@qq.com
                liugh1962815@hotmail.com
                Journal
                Health Inf Sci Syst
                Health Inf Sci Syst
                Health Information Science and Systems
                Springer International Publishing (Cham )
                2047-2501
                27 February 2021
                27 February 2021
                December 2021
                : 9
                : 1
                : 11
                Affiliations
                [1 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Knowledge Representation and Reasoning (KR&R) Group, , Vrije Universiteit Amsterdam, ; Amsterdam, Netherlands
                [2 ]GRID grid.412787.f, ISNI 0000 0000 9868 173X, School of Computer Science and Engineering, , Wuhan University of Science and Technology, ; Wuhan, China
                [3 ]Ztone International BV, Purmerend, The Netherlands
                [4 ]Ztone Fujian, Fuzhou City, China
                [5 ]GRID grid.256112.3, ISNI 0000 0004 1797 9307, Fujian Provincial Maternity and Children’s Hospital, , Affiliated Hospital of Fujian Medical University, ; Fuzhou, China
                [6 ]Engineering Research Center for Medical Data Mining and Application of Fujian, Fujian, China
                Article
                130
                10.1007/s13755-020-00130-8
                7910781
                33680447
                a000904a-7c55-4804-8d09-0b0654824b2f
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits 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/4.0/.

                History
                : 2 October 2020
                : 14 October 2020
                Funding
                Funded by: Vrije Universiteit Amsterdam
                Categories
                Research
                Custom metadata
                © Springer Nature Switzerland AG 2021

                knowledge graph,kawasaki disease,semantic technology

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