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An Automated Method for Gender Information Identification from Clinical Trial Texts

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Health Information Science (HIS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10038))

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Abstract

Gender is fundamental and essential information for eligibility criteria electronic prescreening aiming for recruiting appropriate target population for human studies. Current commonly applied gender architecture contains the problems of incompleteness and ambiguity particularly on transgender. This study designs a flexible and extensible virtual population gender architecture for enhancing trial recruitment. We also propose an automated method for high accurate transgender identification and validation. The method defines and identifies transgender features from free clinical trial text. After that, we apply a context-based strategy to obtain final gender summary. The experiments are based on clinical trials from ClinicalTrials.gov, and results present that the method achieves a True Positive Rate of 0.917 and a True Negative Rate of 1.0 on the clinical trial text, demonstrating its effectiveness in transgender identification.

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Acknowledgements

The work described in this paper was substantially supported by the National Natural Science Foundation of China (grant No. 61403088) and the Innovative School Project in Higher Education of Guangdong, China (grant No. YQ2015062).

Author information

Authors and Affiliations

  1. School of Informatics, Guangdong University of Foreign Studies, Guangzhou, China

    Tianyong Hao & Boyu Chen

  2. Faculty of Built Environment, University of New South Wales, Sydney, Australia

    Yingying Qu

Authors
  1. Tianyong Hao
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  2. Boyu Chen
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  3. Yingying Qu
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Corresponding author

Correspondence to Yingying Qu .

Editor information

Editors and Affiliations

  1. Victoria University , Melbourne, Australia

    Xiaoxia Yin

  2. Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA

    James Geller

  3. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Ye Li

  4. Victoria University , Melbourne, Australia

    Rui Zhou

  5. Centre for Applied Informatics, Victoria University, Melbourne, Australia

    Hua Wang

  6. Centre for Applied Informatics, Victoria University, Melbourne, Australia

    Yanchun Zhang

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© 2016 Springer International Publishing AG

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Hao, T., Chen, B., Qu, Y. (2016). An Automated Method for Gender Information Identification from Clinical Trial Texts. In: Yin, X., Geller, J., Li, Y., Zhou, R., Wang, H., Zhang, Y. (eds) Health Information Science. HIS 2016. Lecture Notes in Computer Science(), vol 10038. Springer, Cham. https://doi.org/10.1007/978-3-319-48335-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-48335-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48334-4

  • Online ISBN: 978-3-319-48335-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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