Using machine learning to identify benign cases with non-definitive biopsy

dc.contributor.author Kuusisto,F en
dc.contributor.author Inês Dutra en
dc.contributor.author Nassif,H en
dc.contributor.author Wu,Y en
dc.contributor.author Klein,ME en
dc.contributor.author Neuman,HB en
dc.contributor.author Shavlik,J en
dc.contributor.author Burnside,ES en
dc.date.accessioned 2018-01-18T15:19:02Z
dc.date.available 2018-01-18T15:19:02Z
dc.date.issued 2013 en
dc.description.abstract When mammography reveals a suspicious finding, a core needle biopsy is usually recommended. In 5% to 15% of these cases, the biopsy diagnosis is non-definitive and a more invasive surgical excisional biopsy is recommended to confirm a diagnosis. The majority of these cases will ultimately be proven benign. The use of excisional biopsy for diagnosis negatively impacts patient quality of life and increases costs to the healthcare system. In this work, we employ a multi-relational machine learning approach to predict when a patient with a non-definitive core needle biopsy diagnosis need not undergo an excisional biopsy procedure because the risk of malignancy is low. © 2013 IEEE. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/6987
dc.identifier.uri http://dx.doi.org/10.1109/healthcom.2013.6720685 en
dc.language eng en
dc.relation 5139 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Using machine learning to identify benign cases with non-definitive biopsy en
dc.type conferenceObject en
dc.type Publication en
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