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

Finn Kuusisto, Ines Dutra, Houssam Nassif, Yirong Wu, Molly E. Klein, Heather B. Neuman, Jude Shavlik, Elizabeth S. Burnside

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013
Pages283-285
Number of pages3
DOIs
StatePublished - Dec 1 2013
Event2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013 - Lisbon, Portugal
Duration: Oct 9 2013Oct 12 2013

Publication series

Name2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013

Other

Other2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013
CountryPortugal
CityLisbon
Period10/9/1310/12/13

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    Kuusisto, F., Dutra, I., Nassif, H., Wu, Y., Klein, M. E., Neuman, H. B., Shavlik, J., & Burnside, E. S. (2013). Using machine learning to identify benign cases with non-definitive biopsy. In 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013 (pp. 283-285). [6720685] (2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013). https://doi.org/10.1109/HealthCom.2013.6720685