Natural Language Processing in Radiology: A Systematic Review

Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source of information for improving clinical care and supporting research. Although radiology reports are stored for communication and documentation of diagno...

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Veröffentlicht in:Radiology Jg. 279; H. 2; S. 329
Hauptverfasser: Pons, Ewoud, Braun, Loes M M, Hunink, M G Myriam, Kors, Jan A
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.05.2016
Schlagworte:
Electronic Health Records
Humans
Information Storage and Retrieval
Natural Language Processing
Radiology
Radiology Information Systems
ISSN:1527-1315, 1527-1315
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Zusammenfassung:Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source of information for improving clinical care and supporting research. Although radiology reports are stored for communication and documentation of diagnostic imaging, harnessing their potential requires efficient and automated information extraction: they exist mainly as free-text clinical narrative, from which it is a major challenge to obtain structured data. Natural language processing (NLP) provides techniques that aid the conversion of text into a structured representation, and thus enables computers to derive meaning from human (ie, natural language) input. Used on radiology reports, NLP techniques enable automatic identification and extraction of information. By exploring the various purposes for their use, this review examines how radiology benefits from NLP. A systematic literature search identified 67 relevant publications describing NLP methods that support practical applications in radiology. This review takes a close look at the individual studies in terms of tasks (ie, the extracted information), the NLP methodology and tools used, and their application purpose and performance results. Additionally, limitations, future challenges, and requirements for advancing NLP in radiology will be discussed.
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ISSN:1527-1315
1527-1315
DOI:10.1148/radiol.16142770