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Textual inference for eligibility criteria resolution in clinical trials.

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dc.contributor.author Shivade, Chaitanya
dc.contributor.author Hebert, Courtney
dc.contributor.author Lopetegui, Marcelo
dc.contributor.author De Marneffe, Marie-Catherine
dc.contributor.author Fosler-Lussier, Eric
dc.contributor.author Lai, Albert
dc.date.accessioned 2016-05-18T17:22:26Z
dc.date.available 2016-05-18T17:22:26Z
dc.date.issued 2015
dc.identifier.citation Journal of Biomedical Informatics, Decembre 2015, vol. 58, p.S211-218. es_CL
dc.identifier.uri http://dx.doi.org/10.1016/j.jbi.2015.09.008 es_CL
dc.identifier.uri http://hdl.handle.net/11447/277
dc.description.abstract Clinical trials are essential for determining whether new interventions are effective. In order to determine the eligibility of patients to enroll into these trials, clinical trial coordinators often perform a manual review of clinical notes in the electronic health record of patients. This is a very time-consuming and exhausting task. Efforts in this process can be expedited if these coordinators are directed toward specific parts of the text that are relevant for eligibility determination. In this study, we describe the creation of a dataset that can be used to evaluate automated methods capable of identifying sentences in a note that are relevant for screening a patient's eligibility in clinical trials. Using this dataset, we also present results for four simple methods in natural language processing that can be used to automate this task. We found that this is a challenging task (maximum F-score=26.25), but it is a promising direction for further research. es_CL
dc.language.iso en_US es_CL
dc.publisher Elsevier Inc es_CL
dc.subject Clinical trials es_CL
dc.subject Electronic health records es_CL
dc.subject Natural language processing es_CL
dc.subject Textual inference es_CL
dc.title Textual inference for eligibility criteria resolution in clinical trials. es_CL
dc.type Artículo es_CL


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