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Electronic medical record contains a lot of medical knowledge and patient' s health information. The structuralization and information extraction of obstetric electronic medical records is of great significance on clinical decision and the bearing health. The structural characteristics and content of Chinese obstetric electronic medical records were analyzed. The EMR data was cleaned and structuralized by using the rule-base method. Then the electronic medical records of different treatment types were automatically classified by using the maximum entropy model and rule-based methods. And the F value reached88. 16%. At last,in order to further use electronic medical records for information extraction and knowledge mining,the support vector machine model,in which a phrase was taken as a unit and similarity as benchmark,was used to remove the repetition in first course of disease records. And the result was that68. 6% of the reduplicate and similar phrases were deleted from the records. It was expected that this study could contribute to the further research on the information extraction from obstetrics electronic medical records.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2017005
China Classification Code:TP391.1
Citation Information:
[1]ZHANG Kunli,MA Hongchao,ZHAO Yueshu ,et al.The Study of Chinese Obstetric Electronic Medical Records Based on Natural Language Processing[J].Journal of Zhengzhou University(Natural Science Edition),2017,49(04):40-45.DOI:10.13705/j.issn.1671-6841.2017005.
Fund Information:
973课题(2014CB340504);; 国家自然科学基金项目(61402419,60970083);; 国家社会科学基金项目(14BYY096);; 河南省科技厅基础研究项目(142300410231,142300410308)
2017-06-12
2017-06-12
2017-06-12