Numerical eligibility criteria in clinical protocols: annotation, automatic detection and interpretation

Vincent Claveau 1 Lucas Oliveira 2 Guillaume Bouzillé 3 Marc Cuggia 3 Claudia Cabral Moro 2 Natalia Grabar 4
1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA_D6 - MEDIA ET INTERACTIONS
2 PUCPR - Pontifical Catholic University of Paraná
3 LTSI - Laboratoire Traitement du Signal et de l'Image
4 STL - Savoirs, Textes, Langage (STL) - UMR 8163
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 CV U 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 : Numerical Criteria Natural Language Processing Supervised Learning Clinical Trials Patient Eligibility
Type de document :
Communication dans un congrès
AIME 2017 - 16th Conference in Artificial Intelligence in Medecine, Jun 2017, Vienne, Austria. Springer, 10259, pp.203-208, LNCS. 〈10.1007/978-3-319-59758-4_22〉
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Contributeur : Vincent Claveau <>
Soumis le : mardi 21 novembre 2017 - 18:29:48
Dernière modification le : mercredi 16 mai 2018 - 11:24:14

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Vincent Claveau, Lucas Oliveira, Guillaume Bouzillé, Marc Cuggia, Claudia Cabral Moro, et al.. Numerical eligibility criteria in clinical protocols: annotation, automatic detection and interpretation. AIME 2017 - 16th Conference in Artificial Intelligence in Medecine, Jun 2017, Vienne, Austria. Springer, 10259, pp.203-208, LNCS. 〈10.1007/978-3-319-59758-4_22〉. 〈hal-01618930v2〉

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