AMIA Annu Symp Proc - Integrating machine learning and physician knowledge to improve the accuracy of breast biopsy.

Tópicos

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Resumo

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.

Resumo Limpo

work show combin physician rule machin learn rule may improv perform classifi predict whether breast cancer miss percutan imageguid breast core needl biopsi subsequ refer breast core biopsi specif show advic form logic rule deriv subspecialti ie fellowship train breast radiologist subsequ refer physician can guid search induct logic program system improv perform learn classifi dataset consecut benign breast core biopsi result along correspond mammograph find contain case deem nondefinit multidisciplinari panel physician upgrad malign diseas surgeri goal predict upgrad prospect avoid surgeri women breast cancer result trend toward signific show evid induct logic program may produc better result task tradit proposit algorithm default paramet moreov show ad knowledg physician learn process may improv perform learn classifi train data

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