Advertisement

An Automated Method for Gender Information Identification from Clinical Trial Texts

  • Tianyong Hao
  • Boyu Chen
  • Yingying QuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10038)

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.

Keywords

Gender identification Clinical trial texts Patient recruitment 

Notes

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).

References

  1. 1.
    Fernández-Arroyo, S., Camps, J., Menendez, J.A., Joven, J.: Managing hypertension by polyphenols. Planta Med. 81(08), 624–629 (2015)CrossRefGoogle Scholar
  2. 2.
    Tua, S.W., Pelega, M.B., Carinic, S., Bobakc, M., Rossd, J., Rubine, D., Simc, I.: A practical method for transforming free-text eligibility criteria into computable criteria. J. Biomed. Inform. 44(2), 239–250 (2011)CrossRefGoogle Scholar
  3. 3.
    Jeffrey, M.F., William, G., Jonathan, M., Howard, S., Tanya, B., Monica, M.H.: The design and implementation of an open-source, data-driven cohort recruitment system: the Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN). J. Am. Med. Inf. Assoc. 19(e1), e68–e75 (2012)CrossRefGoogle Scholar
  4. 4.
    Weng, C., Wu, X., Luo, Z., Boland, M.R., Theodoratos, D., Johnson, S.B.: EliXR: an approach to eligibility criteria extraction and representation. J. Am. Med. Inform. Assoc. 18(Suppl 1), i116–i124 (2011)CrossRefGoogle Scholar
  5. 5.
    Hao, T., Rusanov, A., Boland, M.R., Weng, C.: Clustering clinical trials with similar eligibility criteria features. J. Biomed. Inform. 52, 112–120 (2014)CrossRefGoogle Scholar
  6. 6.
    Schroeder, M.A., Robb, L.A.: Criteria for gender and age. Techniques for wildlife investigations and management, pp. 303–338 (2005)Google Scholar
  7. 7.
    Weng, C., Tu, S.W., Sim, I., Richesson, R.: Formal representation of eligibility criteria: a literature review. J. Biomed. Inform. 43(3), 451–467 (2010)CrossRefGoogle Scholar
  8. 8.
    Lonsdale, D., Tustison, C., Parker, C., Embley, D.W.: Formulating queries for assessing clinical trial eligibility. In: Kop, C., Fliedl, G., Mayr, H.C., Métais, E. (eds.) NLDB 2006. LNCS, vol. 3999, pp. 82–93. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Clinicaltrials.gov-A service of the U.S. National Institutes of Health. http://clinicaltrials.gov/ Accessed in 2016
  10. 10.
    Lynch, D.R., Pandolfo, M., Schulz, J.B., Perlman, S., Delatycki, M.B., Payne, R.M., Shaddy, R., Fischbeck, K.H., Farmer, J., Kantor, P., Raman, S.V., Hunegs, L., Odenkirchen, J., Miller, K., Kaufmann, P.: Common data elements for clinical research in friedreich’s ataxia. Mov. Disord. 28(2), 190–195 (2013)CrossRefGoogle Scholar
  11. 11.
    Cichocki, M.: HIV risk in the transgender men and women - understanding why transgender people are at increased HIV risk. https://www.verywell.com/hiv-risk-and-the-transgender-population-47883. Accessed in 2016
  12. 12.
    Gates, G.J.: How many people are lesbian, gay, bisexual, and transgender? The Williams Institute, UCLA (2011)Google Scholar
  13. 13.
    Lalkhen, A.G., McCluskey, A.: Clinical tests: sensitivity and specificity. BJA: CEACCP 8(6), 221–223 (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of InformaticsGuangdong University of Foreign StudiesGuangzhouChina
  2. 2.Faculty of Built EnvironmentUniversity of New South WalesSydneyAustralia

Personalised recommendations