Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings.

By E. Burnside, Jesse Davis, J. Chhatwal, O. Alagoz, M. Lindstrom, B. Geller, B. Littenberg, K. Shaffer, Charles E. Kahn Jr., C. D. Page. 2009

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A case-based interpretable deep learning model for classification of mass lesions in digital mammography (2021, background): “Some studies have reported better performance in predicting malignancy from BI-RADS features [19, 6, 7].”
Advancing the Science for Active Surveillance: Rationale and Design for the Observational Medical Outcomes Partnership (2010, methodology): “Statistical relational learning (34, 35) University of Wisconsin This method adapts a machine-learning approach to work directly with relational data distributed across many tables to extract “rules” that define observed phenomena, such as drug–condition relationships.”
Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors (2010, methodology): “The risk score (the current probability of cancer) can be estimated by either a CADx model (Ayer et al. 2010, Burnside et al. 2009, Chhatwal et al. 2009) or asking a radiologist.”