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    @Article{OJIOT_2020v6i1n02_BoMa,
        title     = {Assuring Privacy-Preservation in Mining Medical Text Materials for COVID-19 Cases - A Natural Language Processing Perspective},
        author    = {Bo Ma and
                     Jinsong Wu and
                     Shuang Song and
                     William Liu},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2020},
        volume    = {6},
        number    = {1},
        pages     = {6--13},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020080219332153513307},
        urn       = {urn:nbn:de:101:1-2020080219332153513307},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Currently, there is a very large volume of Covid-19 related medical data that have been stored in cloud based systems and made available for studing the disease dynamics. without any privacy-preservation. In order to reduce possible privacy leakage and also accommodate massive medical reports with high efficiencies, we proposed a privacypreserving word embody-based text classification method for mining COVID-19 medical documents. It uses the recurrent neural network deep learning algorithm according to the identified internal hiding centralization pattern. In addition, a new model-fusion method is proposed for the continuous improvement of the system performance.The extensive numerical studies have demonstrated that the classifier of the proposed system has superior performance via integrating with the keywords extraction approach. Moreover, the advanced new model does not only accurately capture the keyword patterns but also effectively capture the analogical hierarchy structure of the pathology related datasets with lower computational complexity.}
    }