Dutra I, Nassif H, Page D, Shavlik J, Strigel R M, Wu Y, Elezaby M E, Burnside E
University of Porto, Porto, Portugal.
AMIA Annu Symp Proc. 2011;2011:349-55. Epub 2011 Oct 22.
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 physicians") 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.
在这项工作中，我们表明，将医生规则和机器学习规则相结合，可以提高预测经皮图像引导下乳腺粗针穿刺活检（以下简称“乳腺粗针活检”）是否漏诊乳腺癌的分类器的性能。具体而言，我们展示了由亚专业（即接受过专科培训的乳腺放射科医生，以下简称“我们的医生”）推导的逻辑规则形式的建议如何在归纳逻辑编程系统中指导搜索，并提高学习到的分类器的性能。我们的数据集包含890例连续的乳腺粗针活检良性结果以及相应的乳腺钼靶检查结果，其中94例被多学科医生小组判定为不确定，其中15例在手术时被升级为恶性疾病。我们的目标是前瞻性地预测升级情况，并避免对没有乳腺癌的女性进行手术。我们的结果，其中一些趋于显著，表明有证据表明归纳逻辑编程在此任务上可能比具有默认参数的传统命题算法产生更好的结果。此外，我们表明，将我们医生的知识添加到学习过程中，可以提高仅基于数据训练的学习到的分类器的性能。
