Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/6987
Title: Using machine learning to identify benign cases with non-definitive biopsy
Authors: Kuusisto,F
Inês Dutra
Nassif,H
Wu,Y
Klein,ME
Neuman,HB
Shavlik,J
Burnside,ES
Issue Date: 2013
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.
URI: http://repositorio.inesctec.pt/handle/123456789/6987
http://dx.doi.org/10.1109/healthcom.2013.6720685
metadata.dc.type: conferenceObject
Publication
Appears in Collections:CRACS - Articles in International Conferences

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