J Am Med Inform Assoc - Automatic abstraction of imaging observations with their characteristics from mammography reports.

Tópicos

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Resumo

CKGROUND: Radiology reports are usually narrative, unstructured text, a format which hinders the ability to input report contents into decision support systems. In addition, reports often describe multiple lesions, and it is challenging to automatically extract information on each lesion and its relationships to characteristics, anatomic locations, and other information that describes it. The goal of our work is to develop natural language processing (NLP) methods to recognize each lesion in free-text mammography reports and to extract its corresponding relationships, producing a complete information frame for each lesion.MATERIALS AND METHODS: We built an NLP information extraction pipeline in the General Architecture for Text Engineering (GATE) NLP toolkit. Sequential processing modules are executed, producing an output information frame required for a mammography decision support system. Each lesion described in the report is identified by linking it with its anatomic location in the breast. In order to evaluate our system, we selected 300 mammography reports from a hospital report database.RESULTS: The gold standard contained 797 lesions, and our system detected 815 lesions (780 true positives, 35 false positives, and 17 false negatives). The precision of detecting all the imaging observations with their modifiers was 94.9, recall was 90.9, and the F measure was 92.8.CONCLUSIONS: Our NLP system extracts each imaging observation and its characteristics from mammography reports. Although our application focuses on the domain of mammography, we believe our approach can generalize to other domains and may narrow the gap between unstructured clinical report text and structured information extraction needed for data mining and decision support.

Resumo Limpo

ckground radiolog report usual narrat unstructur text format hinder abil input report content decis support system addit report often describ multipl lesion challeng automat extract inform lesion relationship characterist anatom locat inform describ goal work develop natur languag process nlp method recogn lesion freetext mammographi report extract correspond relationship produc complet inform frame lesionmateri method built nlp inform extract pipelin general architectur text engin gate nlp toolkit sequenti process modul execut produc output inform frame requir mammographi decis support system lesion describ report identifi link anatom locat breast order evalu system select mammographi report hospit report databaseresult gold standard contain lesion system detect lesion true posit fals posit fals negat precis detect imag observ modifi recal f measur conclus nlp system extract imag observ characterist mammographi report although applic focus domain mammographi believ approach can general domain may narrow gap unstructur clinic report text structur inform extract need data mine decis support

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