Discovering Commonly Shared Semantic Concepts of Eligibility Criteria for Learning Clinical Trial Design

  • Tianyong Hao
  • Xieling Chen
  • Guimin HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9412)


Discovering commonly shared semantic concepts of eligibility criteria can facilitate hospitals in recruiting appropriate target population and empower patients with more effective result ranking of concept-based search, as well as assist researchers in understanding clinical trial design. This study aims to identify commonly shared semantic concepts of eligibility criteria through the identification of eligibility criteria concepts for each disease. An automated approach for extracting semantic concepts from eligibility criteria texts is proposed. For each disease, commonly shared semantic concepts are determined for reviewing the commonly shared concepts of clinical trials. Our experiment dataset are 145,745 clinical trials associated with 5,488 different types of diseases on 5,508,491 semantic concepts are extracted with 459,936 being unique. We further analyze its application on assisting researchers in learning disease-specific clinical trial design.


Semantic concepts Eligibility criteria Learning 



This work was supported by National Natural Science Foundation of China (grant No. 61403088).


  1. 1.
    Ross, J., Tu, S., Carini, S., Sim, I.: Analysis of eligibility criteria complexity in clinical trials. In: Proceedings of AMIA Summits on Translational Science 2010, pp. 46-50 (2010)Google Scholar
  2. 2.
    Vellas, B., Pesce, A., Robert, P.H., et al.: AMPA workshop on challenges faced by investigators conducting Alzheimer’s disease clinical trials. Alzheimers Dement. 7(4), e109–e117 (2011)CrossRefGoogle Scholar
  3. 3.
    Campbell, M.K., Snowdon, C., Francis, D., et al.: Recruitment to randomised trials: strategies for trial enrollment and participation study. The STEPS study. Health Technol. Assess. 11(48), iii, ix–105 (2007)Google Scholar
  4. 4.
    Tu, S.W., Peleg, M., Carini, S., et al.: A practical method for transforming free-text eligibility criteria into computable criteria. J. Biomed. Inform. 44(2), 239–250 (2011)CrossRefGoogle Scholar
  5. 5.
    Weng, C., Wu, X., Luo, Z., et al.: EliXR: an approach to eligibility criteria extraction and representation. J. Am. Med. Inform. Assoc. 18(Suppl 1), i116–i124 (2011)CrossRefGoogle Scholar
  6. 6.
    Milian, K., Bucur, A., Teije A.T.: Formalization of clinical trial eligibility criteria: Evaluation of a pattern-based approach. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 1–4 (2012)Google Scholar
  7. 7.
    Boland, M.R., Miotto, R., Gao, J., Weng, C.: Feasibility of feature-based indexing, clustering, and search of clinical trials. A case study of breast cancer trials from Methods Inf. Med. 52(5), 382–394 (2013)CrossRefGoogle Scholar
  8. 8.
    Manley, G.T., Diaz-Arrastia, R., Brophy, M., et al.: Common data elements for traumatic brain injury: recommendations from the interagency working group on demographics and clinical assessment. Arch. Phys. Med. Rehabil. 91(11), 1641–1649 (2010)CrossRefGoogle Scholar
  9. 9.
    National institute of health - NINDS Common Data Elements. Accessed 2015
  10. 10.
  11. 11.
    Fink, E., Kokku, P.K., Nikiforou, S., et al.: Selection of patients for clinical trials: an interactive web-based system. Artif. Intell. Med. 31, 241–254 (2004)CrossRefGoogle Scholar
  12. 12.
    NerveCenter: NINDS common data element project: a long-awaited breakthrough in streamlining trials. Ann. Neurol. 68(1), A11–A13 (2010)Google Scholar
  13. 13.
    Grinnon, S.T., Miller, K., Marler, J.R., et al.: National institute of neurological disorders and stroke common data element project - approach and methods. Clin. Trials 9(3), 322–329 (2012)CrossRefGoogle Scholar
  14. 14.
    Loring, D.W., Lowenstein, D.H., Barbaro, N.M., et al.: Common data elements in epilepsy research: development and implementation of the NINDS epilepsy FST project. Epilepsia 52(6), 1186–1191 (2011)CrossRefGoogle Scholar
  15. 15.
    Luo, Z., Miotto, R., Weng, C.: A human-computer collaborative approach to identifying common data elements in clinical trial eligibility criteria. J. Biomed. Inform. 46, 33–39 (2013)CrossRefGoogle Scholar
  16. 16.
    Miotto, R., Weng, C.: Unsupervised mining of frequent tags for clinical eligibility text indexing. J. Biomed. Inform. 46(6), 1145–1151 (2013)CrossRefGoogle Scholar
  17. 17.
    UMLS - Unified Medical Language System. Accessed 2015
  18. 18.
    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
  19. 19.
    Lee-Smeltzer, K.H.: Finding the needle: controlled vocabularies, resource discovery, and Dublin Core. Libr. Collect. Acquis. Techn. Serv. 24(2), 205–215 (2000)CrossRefGoogle Scholar
  20. 20.
    Miotto, R., Jiang, S., Weng, C.: eTACTS: a method for dynamically filtering clinical trial search results. J. Biomed. Inform. 46(6), 1060–1067 (2013)CrossRefGoogle Scholar
  21. 21.
    Hao, T., Weng, C.: Adaptive semantic tag mining from heterogeneous clinical research texts. Methods Inf. Med. 54(2), 164–170 (2015)CrossRefGoogle Scholar
  22. 22.
    Extracting key phrases with NLTK in Python, GitHub Gist. Accessed 2015
  23. 23.
    The Stanford Parser: A statistical parser. Accessed 2015

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Guangdong University of Foreign StudiesGuangzhouChina
  2. 2.Guilin University of Electronic TechnologyGuilinChina

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