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

Journal of Healthcare Engineering
Wencheng SunGuoyan Wang

Abstract

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 ...Continue Reading

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Citations

Mar 6, 2019·International Journal of Environmental Research and Public Health·Berit I HelgheimAna Lucia Martins
May 7, 2019·Frontiers in Medicine·Michela AssaleFederico Cabitza
Apr 2, 2020·Journal of Korean Medical Science·Kye Hwa LeeEun Young Song
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Jul 17, 2021·Journal of Sleep Research·Christophe GauldJean-Arthur Micoulaud-Franchi

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Software Mentioned

MMSEG4J
ProMiner
Micromedex
cloud
LTP
imdict
HTTPCWS
ICTCLASs
ICTCLAS
DXplains

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