Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
A Set of Electronic Medical Records, VanDon Hospital, Vietnam, 24 February 2016
Adnan, M., Warren, J., Orr, M.: Iterative refinement of SemLink to enhance patient readability of discharge summaries. In: Grain, H., Schaper, L.K. (eds.) Health Informatics: Digital Health Service Delivery - The Future is Now!, pp. 128–134 (2013)
Berman, J.J.: Pathology abbreviated: a long review of short terms. Arch. Pathol. Lab. Med. 128, 347–352 (2004)
Collard, B., Royal, A.: The use of abbreviations in surgical note keeping. Ann. Med. Surg. 4, 100–102 (2015)
Henriksson, A., Moen, H., Skeppstedt, M., Daudaravičius, V., Duneld, M.: Synonym extraction and abbreviation expansion with ensembles of semantic spaces. J. Biomed. Semant. 5(6), 1–25 (2014)
Kim, Y., Hurdle, J., Meystre, S.M.: Using UMLS lexical resources to disambiguate abbreviations in clinical text. In: AMIA Annual Symposium Proceedings, pp. 715–722 (2011)
Kim, J.-B., Oh, H.-S., Nam, S.-S., Myaeng, S.-H.: Using candidate exploration and ranking for abbreviation resolution in clinical documents. In: Proceedings of the 2013 International Conference on Healthcare Informatics, pp. 317–326 (2013)
Kim, M.-Y., Xu, Y., Zaiane, O.R., Goebel, R.: Recognition of patient-related named entities in noisy tele-health texts. ACM Trans. Intell. Syst. Technol. 6(4), 59:1–59:23 (2015)
Kim, S., Yoon, J.: Link-topic model for biomedical abbreviation disambiguation. J. Biomed. Inform. 53, 367–380 (2015)
Kreuzthaler, M., Schulz, S.: Detection of sentence boundaries and abbreviations in clinical narratives. BMC Med. Inform. Decis. Making 15, 1–13 (2015)
Liu, Y., Ge, T., Mathews, K.S., Ji, H., McGuinness, D.L.: Exploiting task-oriented resources to learn word embeddings for clinical abbreviation expansion. In: Proceedings of the 2015 Workshop on Biomedical Natural Language Processing, pp. 92–97 (2015)
Long, W.J.: Parsing free text nursing notes. In: AMIA Annual Symposium Proceedings, p. 917 (2003)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Workshop Proceedings of the International Conference on Learning Representations (2013)
Moon, S., Berster, B.T., Xu, H., Cohen, T.: Word sense disambiguation of clinical abbreviations with hyperdimensional computing. In: AMIA Annual Symposium Proceedings, pp. 1007–1016 (2013)
Moon, S., McInnes, B., Melton, G.B.: Challenges and practical approaches with word sense disambiguation of acronyms and abbreviations in the clinical domain. Healthc. Inform. Res. 21(1), 35–42 (2015)
Moon, S., Pakhomov, S., Liu, N., Ryan, J.O., Melton, G.M.: A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources. J. Am. Med. Inform. Assoc. 21, 299–307 (2014)
Pakhomov, S., Pedersen, T., Chute, C.G.: Abbreviation and acronym disambiguation in clinical discourse. In: AMIA Annual Symposium Proceedings, pp. 589–593 (2005)
Weka 3, Data Mining Software in Java. http://www.cs.waikato.ac.nz/ml/weka. Accessed on 22 February 2016
Wong, W., Glance, D.: Statistical semantic and clinician confidence analysis for correcting abbreviations and spelling errors in clinical progress notes. Artif. Intell. Med. 53, 171–180 (2011)
Word2VecJava. https://github.com/medallia/Word2VecJava. Accessed on 22 February 2016
Wu, Y., Denny, J.C., Rosenbloom, S.T., Miller, R.A., Giuse, D.A., Xu, H.: A comparative study of current clinical natural language processing systems on handling abbreviations in discharge summaries. In: AMIA Annual Symposium Proceedings, pp. 997–1003 (2012)
Wu, Y., Rosenbloom, S.T., Denny, J.C., Miller, R.A., Mani, S., Giuse, D.A., Xu, H.: Detecting abbreviations in discharge summaries using machine learning methods. In: AMIA Annual Symposium Proceedings, pp. 1541–1549 (2011)
Wu, Y., Tang, B., Jiang, M., Moon, S., Denny, J.C., Xu, H.: Clinical acronym/abbreviation normalization using a hybrid approach. In: CLEF (2013)
Wu, Y., Xu, J., Zhang, Y., Xu, H.: Clinical abbreviation disambiguation using neural word embeddings. In: Proceedings of the 2015 Workshop on Biomedical Natural Language Processing (BioNLP 2015), pp. 171–176 (2015)
Xu, H., Stetson, P.D., Friedman, C.: A study of abbreviations in clinical notes. In: AMIA Annual Symposium Proceedings, pp. 822–825 (2007)
Xu, H., Stetson, P.D., Friedman, C.: Methods for building sense inventories of abbreviations in clinical notes. J. Am. Med. Inform. Assoc. 16(1), 103–108 (2009)
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Vo, T.N.C., Cao, T.H., Ho, T.B. (2016). Abbreviation Identification in Clinical Notes with Level-wise Feature Engineering and Supervised Learning. In: Ohwada, H., Yoshida, K. (eds) Knowledge Management and Acquisition for Intelligent Systems . PKAW 2016. Lecture Notes in Computer Science(), vol 9806. Springer, Cham. https://doi.org/10.1007/978-3-319-42706-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-42706-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42705-8
Online ISBN: 978-3-319-42706-5
eBook Packages: Computer ScienceComputer Science (R0)