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  • PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking

    Author(s)
    Wang, Meng
    Zhang, Jiaheng
    Liu, Jun
    Hu, Wei
    Wang, Sen
    Li, Xue
    Liu, Wenqiang
    Griffith University Author(s)
    Wang, Sen
    Year published
    2017
    Metadata
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    Abstract
    Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient’s symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis, and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogeneous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart ...
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    Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient’s symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis, and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogeneous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.
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    Journal Title
    Lecture Notes in Computer Science
    Volume
    10588
    DOI
    https://doi.org/10.1007/978-3-319-68204-4_23
    Subject
    Other information and computing sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/369285
    Collection
    • Journal articles

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