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    An effective disease risk indicator tool

    Taha, K. and Yoo, Paul D. (2020) An effective disease risk indicator tool. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) , ISSN 2694-0604.

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    Abstract

    Each mixture of deficient molecular families of a specific disease induces the disease at a different time frame in the future. Based on this, we propose a novel methodology for personalizing a person’s level of future susceptibility to a specific disease by inferring the mixture of his/her molecular families, whose combined deficiencies is likely to induce the disease. We implemented the methodology in a working system called DRIT, which consists of the following components: logic inferencer, information extractor, risk indicator, and interrelationship between molecular families modeler. The information extractor takes advantage of the exponential increase of biomedical literature to extract the common biomarkers that test positive among most patients with a specific disease. The logic inferencer transforms the hierarchical interrelationships between the molecular families of a disease into rule-based specifications. The interrelationship between molecular families modeler models the hierarchical interrelationships between the molecular families, whose biomarkers were extracted by the information extractor. It employs the specification rules and the inference rules for predicate logic to infer as many as possible probable deficient molecular families for a person based on his/her few molecular families, whose biomarkers tested positive by medical screening. The risk indicator outputs a risk indicator value that reflects a person’s level of future susceptibility to the disease. We evaluated DRIT by comparing it experimentally with a comparable method. Results revealed marked improvement.

    Metadata

    Item Type: Article
    Additional Information: (c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Paul Yoo
    Date Deposited: 02 Dec 2020 12:01
    Last Modified: 09 Aug 2021 10:00
    URI: https://eprints.bbk.ac.uk/id/eprint/31652

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