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      How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective

      , , , ,
      Computers in Biology and Medicine
      Elsevier BV

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          Blockchain: A Panacea for Healthcare Cloud-Based Data Security and Privacy?

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            A review of feature selection methods in medical applications

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              Data Processing and Text Mining Technologies on Electronic Medical Records: A Review

              Currently, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed, and treatment results. EMR has been recognized as a valuable resource for large-scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation, and data reduction. For semistructured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (named-entity recognition) and RE (relation extraction). This paper focuses on the process of EMR processing and emphatically analyzes the key techniques. In addition, we make an in-depth study on the applications developed based on text mining together with the open challenges and research issues for future work.
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                Author and article information

                Journal
                Computers in Biology and Medicine
                Computers in Biology and Medicine
                Elsevier BV
                00104825
                August 2022
                August 2022
                : 147
                : 105726
                Article
                10.1016/j.compbiomed.2022.105726
                f767110a-5bfd-45da-be7b-49ff19fecee7
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/


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