Automatic detection of actionable findings and communication mentions in radiology reports using natural language processing.

Journal: European radiology

Published Date: Jan 6, 2022

Abstract

OBJECTIVES: To develop and validate classifiers for automatic detection of actionable findings and documentation of nonroutine communication in routinely delivered radiology reports.

Authors

Jacob J Visser

Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.
Marianne de Vries

Department of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, the Netherlands.
Jan A Kors

Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands j.kors@erasmusmc.nl.

Keywords

Communication Humans Machine Learning Natural Language Processing Radiology

External Resources

View on PubMed Access via DOI PubMed (34989840)

Automatic detection of actionable findings and communication mentions in radiology reports using natural language processing.

Abstract

Authors

Keywords

External Resources

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