Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing.
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ABSTRACT:
SUBMITTER: Hassanpour S
PROVIDER: S-EPMC5422225 | biostudies-literature | 2017 Jun
REPOSITORIES: biostudies-literature
Publications
Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing.
Hassanpour Saeed S Bay Graham G Langlotz Curtis P CP
Journal of digital imaging 20170601 3
We built a natural language processing (NLP) method to automatically extract clinical findings in radiology reports and characterize their level of change and significance according to a radiology-specific information model. We utilized a combination of machine learning and rule-based approaches for this purpose. Our method is unique in capturing different features and levels of abstractions at surface, entity, and discourse levels in text analysis. This combination has enabled us to recognize t ...[more]
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