Abbreviation Identification in Clinical Notes with Level-wise Feature Engineering and Supervised Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9806)


Nowadays, electronic medical records get more popular and significant in medical, biomedical, and healthcare research activities. Their popularity and significance lead to a growing need for sharing and utilizing them from the outside. However, explicit noises in the shared records might hinder users in their efforts to understand and consume the records. One kind of explicit noises that has a strong impact on the readability of the records is a set of abbreviations written in free text in the records because of writing-time saving and record simplification. Therefore, automatically identifying abbreviations and replacing them with their correct long forms are necessary for enhancing their readability and further their sharability. In this paper, our work concentrates on abbreviation identification to lay the foundations for de-noising clinical text with abbreviation resolution. Our proposed solution to abbreviation identification is general, practical, simple but effective with level-wise feature engineering and a supervised learning mechanism. We do level-wise feature engineering to characterize each token that is either an abbreviation or a non-abbreviation at the token, sentence, and note levels to formulate a comprehensive vector representation in a vector space. After that, many open options can be made to build an abbreviation identifier in a supervised learning mechanism and the resulting identifier can be used for automatic abbreviation identification in clinical text of the electronic medical records. Experimental results on various real clinical note types have confirmed the effectiveness of our solution with high accuracy, precision, recall, and F-measure for abbreviation identification.


Electronic medical record Clinical note Abbreviation identification Level-wise feature engineering Supervised learning Word embedding 



This work is funded by Vietnam National University at Ho Chi Minh City under the grant number B2016-42-01. In addition, we would like to thank John von Neumann Institute, Vietnam National University at Ho Chi Minh City, very much to provide us with a very powerful server machine to carry out the experiments. Moreover, this work was completed when the authors were working at Vietnam Institute for Advanced Study in Mathematics, Vietnam. Besides, our thanks go to Dr. Nguyen Thi Minh Huyen and her team at University of Science, Vietnam National University, Hanoi, Vietnam, for external resources used in the experiments and also to the administrative board at VanDon Hospital for their real clinical data and support.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of Technology, Vietnam National UniversityHo Chi Minh CityVietnam
  2. 2.Japan Advanced Institute of Science and TechnologyNomiJapan
  3. 3.John von Neumann Institute, Vietnam National UniversityHo Chi Minh CityVietnam

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