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International Conference on Data Mining and Big Data

DMBD 2017: Data Mining and Big Data pp 409–421Cite as

Extraction of Temporal Events from Clinical Text Using Semi-supervised Conditional Random Fields

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10387))

Abstract

The huge amount of clinical text in Electronic Medical Records (EMRs) has opened a stage for text processing and information extraction for healthcare and medical research. Extracting temporal information in clinical text is much more difficult than the newswire text due to implicit expression of temporal information, domain-specific nature and lack of writing quality, among others. Despite of these constraints, some of the existing works established rule-based, machine learning and hybrid methods to extract temporal information with the help of annotated corpora. However obtaining the annotated corpora is costly, time consuming and requires much manual effort and thus their small size inevitably affects the processing quality. Motivated by this fact, in this work we propose a novel two-stage semi-supervised framework to exploit the abundant unannotated clinical text to automatically detect temporal events and then subsequently improve the stability and the accuracy of temporal event extraction. We trained and evaluated our semi-supervised model with the selected features of testing dataset, resulting F-measure of 89.76% for event extraction.

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Notes

  1. 1.

    https://www.i2b2.org/NLP/TemporalRelations/.

  2. 2.

    https://sourceforge.net/projects/crfpp/.

  3. 3.

    http://mallet.cs.umass.edu/semi-sup-fst.php.

  4. 4.

    https://metamap.nlm.nih.gov/.

  5. 5.

    http://www.nactem.ac.uk/GENIA/tagger/.

  6. 6.

    http://www.nltk.org/api/nltk.tag.html.

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Acknowledgments

This work is partially funded by Vietnam National University at Ho Chi Minh City under the grant number B2015-42-02, and Japan Advanced Institute of Science and Technology under the Data Science Project. Also we thank mayo clinic and Informatics for Integrating Biology and the Bedside (I2B2) organizers for providing access to annotated I2B2 temporal relations corpus.

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Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, Japan

    Gandhimathi Moharasan & Tu-Bao Ho

  2. John Von Neumann Institute, VNU-HCM, Ho Chi Minh City, Vietnam

    Tu-Bao Ho

Authors
  1. Gandhimathi Moharasan
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  2. Tu-Bao Ho
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Corresponding author

Correspondence to Gandhimathi Moharasan .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Moharasan, G., Ho, TB. (2017). Extraction of Temporal Events from Clinical Text Using Semi-supervised Conditional Random Fields. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_41

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  • DOI: https://doi.org/10.1007/978-3-319-61845-6_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61844-9

  • Online ISBN: 978-3-319-61845-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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