Ines Dutra, Houssam Nassif, David Page, Jude Shavlik, Roberta Strigel, Yirong Wu, Mai Elezabi & Elizabeth Burnside (2011).
Integrating Machine Learning and Physician Knowledge to Improve the Accuracy of Breast Biopsy. Proceedings of the American Medical Informatics Association Annual Symposium (AMIA).
Slides (PPTX).

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Abstract:

In this work we show that combining physician rules and machine learned rules may improve the performance of a classifier that predicts whether a breast cancer is missed on percutaneous, image-guided breast core needle biopsy (subsequently referred to as ''breast core biopsy''). Specifically, we show how advice in the form of logical rules, derived by a sub-specialty, i.e. fellowship trained breast radiologists (subsequently referred to as ''our physician'') can guide the search in an inductive logic programming system, and improve the performance of a learned classifier. Our dataset of 890 consecutive benign breast core biopsy results along with corresponding mammographic findings contains 94 cases that were deemed non-definitive by a multidisciplinary panel of physicians, from which 15 were upgraded to malignant disease at surgery. Our goal is to predict upgrade prospectively and avoid surgery in women who do not have breast cancer. Our results, some of which trended toward significance, show evidence that inductive logic programming may produce better results for this task than traditional propositional algorithms with default parameters. Moreover, we show that adding knowledge from our physicians into the learning process may improve the performance of the learned classifier trained only on data.

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