Version 1
: Received: 11 August 2017 / Approved: 15 August 2017 / Online: 15 August 2017 (05:46:43 CEST)
How to cite:
Sun, W.; Liu, F.; Cai, Z.; Fang, S.; Wang, G. A Survey of Data Processing of EMR (Electronic Medical Record) Based on Data Mining. Preprints2017, 2017080055. https://doi.org/10.20944/preprints201708.0055.v1
Sun, W.; Liu, F.; Cai, Z.; Fang, S.; Wang, G. A Survey of Data Processing of EMR (Electronic Medical Record) Based on Data Mining. Preprints 2017, 2017080055. https://doi.org/10.20944/preprints201708.0055.v1
Sun, W.; Liu, F.; Cai, Z.; Fang, S.; Wang, G. A Survey of Data Processing of EMR (Electronic Medical Record) Based on Data Mining. Preprints2017, 2017080055. https://doi.org/10.20944/preprints201708.0055.v1
APA Style
Sun, W., Liu, F., Cai, Z., Fang, S., & Wang, G. (2017). A Survey of Data Processing of EMR (Electronic Medical Record) Based on Data Mining. Preprints. https://doi.org/10.20944/preprints201708.0055.v1
Chicago/Turabian Style
Sun, W., Shengqun Fang and Guoyan Wang. 2017 "A Survey of Data Processing of EMR (Electronic Medical Record) Based on Data Mining" Preprints. https://doi.org/10.20944/preprints201708.0055.v1
Abstract
At present, 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 semi-structured 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). In this paper, we introduce the process of EMR processing, including data collection, data preprocessing, data mining, evaluation and knowledge application, analyze the current status of the key technologies, such as data preprocessing and data mining, and provide an overview of the application domains and prospects of EMR mining technologies. Finally, we summarize the existing problems in the research of EMR mining, and review the development trends.
Keywords
EMR; data preprocessing; text mining; information extraction; medical decision support system
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
