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An Interactive Knowledge Graph Based Platform for COVID-19 Clinical Research

Published: 15 February 2022 Publication History

Abstract

Since the first identified case of COVID-19 in December 2019, a plethora of pharmaceuticals and therapeutics have been tested for COVID-19 treatment. While medical advancements and breakthroughs are well underway, the sheer number of studies, treatments, and associated reports makes it extremely challenging to keep track of the rapidly growing COVID-19 research landscape. While existing scientific literature search systems provide basic document retrieval, they fundamentally lack the ability to explore data, and in addition, do not help develop a deeper understanding of COVID-19 related clinical experiments and findings. As research expands, results do so as well, resulting in a position that is complicated and overwhelming. To address this issue, we present a named entity recognition based framework that accurately extracts COVID-19 related information from clinical test results articles, and generates an efficient and interactive visual knowledge graph. This knowledge graph platform is user friendly, and provides intuitive and convenient tools to explore and analyze COVID-19 research data and results including medicinal performances, side effects and target populations.

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Since the first identified case of COVID-19 in December 2019, a plethora of pharmaceuticals and therapeutics have been tested for COVID-19 treatment. While medical advancements and breakthroughs are well underway, the sheer number of studies, treatments and associated reports make it extremely challenging to keep track of the rapidly growing COVID-19 research landscape. To address this issue, we present a named entity recognition-based framework that accurately extracts COVID-19 related information from clinical test results articles, and generates an efficient and interactive visual knowledge graph. This knowledge graph platform is user-friendly and provides intuitive and convenient tools to explore and analyze COVID-19 research data and results including medicinal performances, side effects, and target populations.

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  • (2024)Bayesian Iterative Prediction and Lexical-based Interpretation for Disturbed Chinese Sentence Pair MatchingProceedings of the ACM Web Conference 202410.1145/3589334.3648149(4618-4629)Online publication date: 13-May-2024
  • (2024)Explanation-based Adversarial Detection with Noise Reduction2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825913(6374-6378)Online publication date: 15-Dec-2024
  • (2023)Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunitiesJournal of Big Data10.1186/s40537-023-00774-910:1Online publication date: 28-May-2023

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    cover image ACM Conferences
    WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
    February 2022
    1690 pages
    ISBN:9781450391320
    DOI:10.1145/3488560
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 15 February 2022

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    Author Tags

    1. clinical results
    2. covid-19
    3. knowledge graph
    4. named entity recognition

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    View all
    • (2024)Bayesian Iterative Prediction and Lexical-based Interpretation for Disturbed Chinese Sentence Pair MatchingProceedings of the ACM Web Conference 202410.1145/3589334.3648149(4618-4629)Online publication date: 13-May-2024
    • (2024)Explanation-based Adversarial Detection with Noise Reduction2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825913(6374-6378)Online publication date: 15-Dec-2024
    • (2023)Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunitiesJournal of Big Data10.1186/s40537-023-00774-910:1Online publication date: 28-May-2023

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