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Numerical Eligibility Criteria in Clinical Protocols: Annotation, Automatic Detection and Interpretation

  • Vincent Claveau
  • Lucas Emanuel Silva Oliveira
  • Guillaume Bouzillé
  • Marc Cuggia
  • Claudia Maria Cabral Moro
  • Natalia Grabar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)

Abstract

Clinical trials are fundamental for evaluating therapies and diagnosis techniques. Yet, recruitment of patients remains a real challenge. Eligibility criteria are related to terms but also to patient laboratory results usually expressed with numerical values. Both types of information are important for patient selection. We propose to address the processing of numerical values. A set of sentences extracted from clinical trials are manually annotated by four annotators. Four categories are distinguished: C (concept), V (numerical value), U (unit), O (out position). According to the pairs of annotators, the inter-annotator agreement on the whole annotation sequence CVU goes up to 0.78 and 0.83. Then, an automatic method using CFRs is exploited for creating a supervised model for the recognition of these categories. The obtained F-measure is 0.60 for C, 0.82 for V, and 0.76 for U.

Keywords

Natural language processing Supervised learning Clinical trials Patient eligibility Numerical criteria 

Notes

Acknowledgements

This work was partly funded by CNRS-CONFAP project FIGTEM for Franco-Brazilian collaborations and a French government support granted to the CominLabs LabEx managed by the ANR in Investing for the Future program under reference ANR-10-LABX-07-01.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Vincent Claveau
    • 1
  • Lucas Emanuel Silva Oliveira
    • 2
  • Guillaume Bouzillé
    • 3
  • Marc Cuggia
    • 3
  • Claudia Maria Cabral Moro
    • 2
  • Natalia Grabar
    • 4
  1. 1.IRISA - CNRSRennesFrance
  2. 2.PUCPR - Pontifícia Universidade Católica do ParanáCuritibaBrazil
  3. 3.INSERM/LTSI, HBD; CHU de Rennes; Université Rennes 2RennesFrance
  4. 4.CNRS, Univ. Lille, UMR 8163 - STL - Savoirs Textes LangageLilleFrance

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