Automated Radiology Report Summarization Using an Open-Source Natural Language Processing Pipeline.
J Digit Imaging
; 31(2): 185-192, 2018 04.
Article
em En
| MEDLINE
| ID: mdl-29086081
ABSTRACT
Diagnostic radiologists are expected to review and assimilate findings from prior studies when constructing their overall assessment of the current study. Radiology information systems facilitate this process by presenting the radiologist with a subset of prior studies that are more likely to be relevant to the current study, usually by comparing anatomic coverage of both the current and prior studies. It is incumbent on the radiologist to review the full text report and/or images from those prior studies, a process that is time-consuming and confers substantial risk of overlooking a relevant prior study or finding. This risk is compounded when patients have dozens or even hundreds of prior imaging studies. Our goal is to assess the feasibility of natural language processing techniques to automatically extract asserted and negated disease entities from free-text radiology reports as a step towards automated report summarization. We compared automatically extracted disease mentions to a gold-standard set of manual annotations for 50 radiology reports from CT abdomen and pelvis examinations. The automated report summarization pipeline found perfect or overlapping partial matches for 86% of the manually annotated disease mentions (sensitivity 0.86, precision 0.66, accuracy 0.59, F1 score 0.74). The performance of the automated pipeline was good, and the overall accuracy was similar to the interobserver agreement between the two manual annotators.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Pelve
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Processamento de Linguagem Natural
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Radiografia Abdominal
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Tomografia Computadorizada por Raios X
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Sistemas de Informação em Radiologia
Tipo de estudo:
Guideline
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Observational_studies
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Risk_factors_studies
Limite:
Adolescent
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Adult
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Aged
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Aged80
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Child
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Child, preschool
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article