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

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

Kuusisto, F (Author) Other; Dutra, I (Author) FCUP; Nassif, H (Author) Other; Wu, Y (Author) Other; Klein, ME (Author) Other; Neuman, HB (Author) Other; Shavlik, J (Author) Other; Burnside, ES (Author) Other

Conference proceedings International

Title: 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013

Pages: 283-285

2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013

Lisbon, 9 October 2013 through 12 October 2013

Indexing

Scopus - 3 Citations

Scientific classification

FOS: Natural sciences

Other information

Authenticus ID: P-009-764

DOI: 10.1109/healthcom.2013.6720685

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific