What Influences the Way Radiologists Express Themselves in Their Reports? A Quantitative Assessment Using Natural Language Processing.
Emergency radiology
Natural language processing
Principal component analysis
Structured reports
Teleradiology
Text mining
Journal
Journal of digital imaging
ISSN: 1618-727X
Titre abrégé: J Digit Imaging
Pays: United States
ID NLM: 9100529
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
14
10
2021
accepted:
09
03
2022
revised:
07
03
2022
pubmed:
24
3
2022
medline:
23
9
2022
entrez:
23
3
2022
Statut:
ppublish
Résumé
Although using standardized reports is encouraged, most emergency radiological reports in France remain in free-text format that can be mined with natural language processing for epidemiological purposes, activity monitoring or data collection. These reports are obtained under various on-call conditions by radiologists with various backgrounds. Our aim was to investigate what influences the radiologists' written expressions. To do so, this retrospective multicentric study included 30,227 emergency radiological reports of computed tomography scans and magnetic resonance imaging involving exactly one body region, only with pathological findings, interpreted from 2019-09-01 to 2020-02-28 by 165 radiologists. After text pre-processing, one-word tokenization and use of dictionaries for stop words, polarity, sentiment and uncertainty, 11 variables depicting the structure and content of words and sentences in the reports were extracted and summarized to 3 principal components capturing 93.7% of the dataset variance. In multivariate analysis, the 1
Identifiants
pubmed: 35318544
doi: 10.1007/s10278-022-00619-6
pii: 10.1007/s10278-022-00619-6
pmc: PMC8939885
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
993-1007Informations de copyright
© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.
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