BAGGER: An EBL System that Extends and Generalizes Explanations

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Science Foundation / NSF IST 85-1154

Computer Understanding and Generalization of Symbolic Mathematical Calculations

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Science Foundation / NSF IST 85-1154

A Model of Attention Focussing During Problem Solving

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Science Foundation / NSF IST 85-1154

An Explanation-Based Approach to Generalizing Number

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Science Foundation / NSF IST 85-1154

Analyzing Variable Cancellations to Generalize Symbolic Mathematical Calculations

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Science Foundation / NSF IST 85-1154

Acquiring Special Case Schemata in Explanation-Based Learning

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Science Foundation / NSF IST 85-1154

Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer

The purpose of our study is to identify and quantify the association between high breast mass density and breast malignancy using inductive logic programming (ILP) and conditional probabilities, and validate this association in an independent dataset. We ran our ILP algorithm on 62,219 mammographic abnormalities. We set the Aleph ILP system to generate 10,000 rules per malignant finding with a recall >5% and precision >25%. Aleph reported the best rule for each malignant finding. A total of 80 unique rules were learned. A radiologist reviewed all rules and identified potentially interesting rules. High breast mass density appeared in 24% of the learned rules. We confirmed each interesting rule by calculating the probability of malignancy given each mammographic descriptor. High mass density was the fifth highest ranked predictor. To validate the association between mass density and malignancy in an independent dataset, we collected data from 180 consecutive breast biopsies performed between 2005 and 2007. We created a logistic model with benign or malignant outcome as the dependent variable while controlling for potentially confounding factors. We calculated odds ratios based on dichomotized variables. In our logistic regression model, the independent predictors high breast mass density (OR 6.6, CI 2.5–17.6), irregular mass shape (OR 10.0, CI 3.4–29.5), spiculated mass margin (OR 20.4, CI 1.9–222.8), and subject age (β = 0.09, p < 0.0001) significantly predicted malignancy. Both ILP and conditional probabilities show that high breast mass density is an important adjunct predictor of malignancy, and this association is confirmed in an independent data set of prospectively collected mammographic findings