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Automatic inference of BI-RADS final assessment categories from narrative mammography report findings

Publication Type:

Journal Article

Source:

J Biomed Inform, Volume 92, p.103137 (2019)

ISBN:

1532-0480 (Electronic)<br/>1532-0464 (Linking)

Accession Number:

30807833

URL:

https://web.stanford.edu/group/rubinlab/pubs/Banerjee-2019-AutomaticinferenceBI-RADS.pdf

Keywords:

BI-RADS classification, Deep Learning, Distributional semantics, Mammography report, Nlp, Text mining

Abstract:

We propose an efficient natural language processing approach for inferring the BI-RADS final assessment categories by analyzing only the mammogram findings reported by the mammographer in narrative form. The proposed hybrid method integrates semantic term embedding with distributional semantics, producing a context-aware vector representation of unstructured mammography reports. A large corpus of unannotated mammography reports (300,000) was used to learn the context of the key-terms using a distributional semantics approach, and the trained model was applied to generate context-aware vector representations of the reports annotated with BI-RADS category (22,091). The vectorized reports were utilized to train a supervised classifier to derive the BI-RADS assessment class. Even though the majority of the proposed embedding pipeline is unsupervised, the classifier was able to recognize substantial semantic information for deriving the BI-RADS categorization not only on a holdout internal testset and also on an external validation set (1900 reports). Our proposed method outperforms a recently published domain-specific rule-based system and could be relevant for evaluating concordance between radiologists. With minimal requirement for task specific customization, the proposed method can be easily transferable to a different domain to support large scale text mining or derivation of patient phenotype.